From 449cc00244e7f4f965d722dd6d986868c21f0012 Mon Sep 17 00:00:00 2001
From: Fabian Pedregosa <fabian.pedregosa@inria.fr>
Date: Thu, 29 Apr 2010 06:59:48 +0000
Subject: [PATCH] Refactoring on the svm module.
Cleanup innecessary code in svm module, add docstring. Also simplify the bindings and avoid creating new objects in the wrap functions, pass arrays by reference whenever possible.
Change some names: SVC.rho_ --> SVC.intercept_
Also added tests.
git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@710 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8
---
examples/run_all.sh | 7 +
examples/svm/plot_svm_hyperplane.py | 2 +-
scikits/learn/src/libsvm.c | 5082 +++++++++++++++------------
scikits/learn/src/libsvm.pyx | 81 +-
scikits/learn/src/libsvm_helper.c | 39 +-
scikits/learn/svm.py | 204 +-
scikits/learn/tests/test_svm.py | 157 +-
7 files changed, 3194 insertions(+), 2378 deletions(-)
diff --git a/examples/run_all.sh b/examples/run_all.sh
index cc40d77265..85a1f6dbba 100755
--- a/examples/run_all.sh
+++ b/examples/run_all.sh
@@ -6,3 +6,10 @@ for file in `ls *.py`; do
python $file
done
+for file in `ls svm/*.py`; do
+ python $file
+done
+
+for file in `ls em/*.py`; do
+ python $file
+done
diff --git a/examples/svm/plot_svm_hyperplane.py b/examples/svm/plot_svm_hyperplane.py
index f2e930552f..18858b73c3 100644
--- a/examples/svm/plot_svm_hyperplane.py
+++ b/examples/svm/plot_svm_hyperplane.py
@@ -22,7 +22,7 @@ clf.fit(X, Y)
w = np.dot(clf.dual_coef_[0], clf.support_)
a = -w[0]/w[1]
xx = np.linspace(-5, 5)
-yy = a*xx + (clf.rho_[0])/w[1]
+yy = a*xx - (clf.intercept_[0])/w[1]
# plot the parallels to the separating hyperplane that pass through the
# support vectors
diff --git a/scikits/learn/src/libsvm.c b/scikits/learn/src/libsvm.c
index 53e46bec9c..17fb936b42 100644
--- a/scikits/learn/src/libsvm.c
+++ b/scikits/learn/src/libsvm.c
@@ -1,11 +1,11 @@
-/* Generated by Cython 0.11.2 on Thu Mar 4 17:07:19 2010 */
+/* Generated by Cython 0.12 on Tue Apr 27 19:58:59 2010 */
#define PY_SSIZE_T_CLEAN
#include "Python.h"
#include "structmember.h"
#ifndef Py_PYTHON_H
#error Python headers needed to compile C extensions, please install development version of Python.
-#endif
+#else
#ifndef PY_LONG_LONG
#define PY_LONG_LONG LONG_LONG
#endif
@@ -15,6 +15,7 @@
#if PY_VERSION_HEX < 0x02040000
#define METH_COEXIST 0
#define PyDict_CheckExact(op) (Py_TYPE(op) == &PyDict_Type)
+ #define PyDict_Contains(d,o) PySequence_Contains(d,o)
#endif
#if PY_VERSION_HEX < 0x02050000
typedef int Py_ssize_t;
@@ -73,8 +74,13 @@
#endif
#if PY_MAJOR_VERSION >= 3
#define PyBaseString_Type PyUnicode_Type
- #define PyString_Type PyBytes_Type
- #define PyString_CheckExact PyBytes_CheckExact
+ #define PyString_Type PyUnicode_Type
+ #define PyString_CheckExact PyUnicode_CheckExact
+#else
+ #define PyBytes_Type PyString_Type
+ #define PyBytes_CheckExact PyString_CheckExact
+#endif
+#if PY_MAJOR_VERSION >= 3
#define PyInt_Type PyLong_Type
#define PyInt_Check(op) PyLong_Check(op)
#define PyInt_CheckExact(op) PyLong_CheckExact(op)
@@ -89,9 +95,10 @@
#define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask
#define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
#define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y)
+ #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y)
#else
#define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y)
- #define PyBytes_Type PyString_Type
+ #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y)
#endif
#if PY_MAJOR_VERSION >= 3
#define PyMethod_New(func, self, klass) PyInstanceMethod_New(func)
@@ -133,11 +140,11 @@
#include <math.h>
#define __PYX_HAVE_API__libsvm
#include "stdlib.h"
+#include "stdio.h"
#include "numpy/arrayobject.h"
+#include "numpy/ufuncobject.h"
#include "svm.h"
#include "libsvm_helper.c"
-#define __PYX_USE_C99_COMPLEX defined(_Complex_I)
-
#ifdef __GNUC__
#define INLINE __inline__
@@ -147,11 +154,7 @@
#define INLINE
#endif
-typedef struct {PyObject **p; char *s; long n; char is_unicode; char intern; char is_identifier;} __Pyx_StringTabEntry; /*proto*/
-
-
-
-static int __pyx_skip_dispatch = 0;
+typedef struct {PyObject **p; char *s; const long n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/
/* Type Conversion Predeclarations */
@@ -166,6 +169,9 @@ static int __pyx_skip_dispatch = 0;
#define __Pyx_PyBytes_AsString PyBytes_AsString
#endif
+#define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((char*)s)
+#define __Pyx_PyBytes_AsUString(s) ((unsigned char*) __Pyx_PyBytes_AsString(s))
+
#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False))
static INLINE int __Pyx_PyObject_IsTrue(PyObject*);
static INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
@@ -185,6 +191,40 @@ static INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
#endif
#endif
+
+#if !defined(T_ULONGLONG)
+#define __Pyx_T_UNSIGNED_INT(x) \
+ ((sizeof(x) == sizeof(unsigned char)) ? T_UBYTE : \
+ ((sizeof(x) == sizeof(unsigned short)) ? T_USHORT : \
+ ((sizeof(x) == sizeof(unsigned int)) ? T_UINT : \
+ ((sizeof(x) == sizeof(unsigned long)) ? T_ULONG : -1))))
+#else
+#define __Pyx_T_UNSIGNED_INT(x) \
+ ((sizeof(x) == sizeof(unsigned char)) ? T_UBYTE : \
+ ((sizeof(x) == sizeof(unsigned short)) ? T_USHORT : \
+ ((sizeof(x) == sizeof(unsigned int)) ? T_UINT : \
+ ((sizeof(x) == sizeof(unsigned long)) ? T_ULONG : \
+ ((sizeof(x) == sizeof(unsigned PY_LONG_LONG)) ? T_ULONGLONG : -1)))))
+#endif
+#if !defined(T_LONGLONG)
+#define __Pyx_T_SIGNED_INT(x) \
+ ((sizeof(x) == sizeof(char)) ? T_BYTE : \
+ ((sizeof(x) == sizeof(short)) ? T_SHORT : \
+ ((sizeof(x) == sizeof(int)) ? T_INT : \
+ ((sizeof(x) == sizeof(long)) ? T_LONG : -1))))
+#else
+#define __Pyx_T_SIGNED_INT(x) \
+ ((sizeof(x) == sizeof(char)) ? T_BYTE : \
+ ((sizeof(x) == sizeof(short)) ? T_SHORT : \
+ ((sizeof(x) == sizeof(int)) ? T_INT : \
+ ((sizeof(x) == sizeof(long)) ? T_LONG : \
+ ((sizeof(x) == sizeof(PY_LONG_LONG)) ? T_LONGLONG : -1)))))
+#endif
+
+#define __Pyx_T_FLOATING(x) \
+ ((sizeof(x) == sizeof(float)) ? T_FLOAT : \
+ ((sizeof(x) == sizeof(double)) ? T_DOUBLE : -1))
+
#if !defined(T_SIZET)
#if !defined(T_ULONGLONG)
#define T_SIZET \
@@ -222,44 +262,145 @@ static INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*);
static PyObject *__pyx_m;
static PyObject *__pyx_b;
static PyObject *__pyx_empty_tuple;
+static PyObject *__pyx_empty_bytes;
static int __pyx_lineno;
static int __pyx_clineno = 0;
static const char * __pyx_cfilenm= __FILE__;
static const char *__pyx_filename;
static const char **__pyx_f;
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+#if !defined(CYTHON_CCOMPLEX)
+ #if defined(__cplusplus)
+ #define CYTHON_CCOMPLEX 1
+ #elif defined(_Complex_I)
+ #define CYTHON_CCOMPLEX 1
+ #else
+ #define CYTHON_CCOMPLEX 0
+ #endif
+#endif
+
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ #include <complex>
+ #else
+ #include <complex.h>
+ #endif
+#endif
-#ifdef CYTHON_REFNANNY
-typedef struct {
- void (*INCREF)(void*, PyObject*, int);
- void (*DECREF)(void*, PyObject*, int);
- void (*GOTREF)(void*, PyObject*, int);
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-#define __Pyx_FinishRefcountContext() __Pyx_Refnanny->FinishContext(&__pyx_refchk)
+typedef npy_int8 __pyx_t_5numpy_int8_t;
+
+typedef npy_int16 __pyx_t_5numpy_int16_t;
+
+typedef npy_int32 __pyx_t_5numpy_int32_t;
+
+typedef npy_int64 __pyx_t_5numpy_int64_t;
+
+typedef npy_uint8 __pyx_t_5numpy_uint8_t;
+
+typedef npy_uint16 __pyx_t_5numpy_uint16_t;
+
+typedef npy_uint32 __pyx_t_5numpy_uint32_t;
+
+typedef npy_uint64 __pyx_t_5numpy_uint64_t;
+
+typedef npy_float32 __pyx_t_5numpy_float32_t;
+
+typedef npy_float64 __pyx_t_5numpy_float64_t;
+
+typedef npy_long __pyx_t_5numpy_int_t;
+
+typedef npy_longlong __pyx_t_5numpy_long_t;
+
+typedef npy_intp __pyx_t_5numpy_intp_t;
+
+typedef npy_uintp __pyx_t_5numpy_uintp_t;
+
+typedef npy_ulong __pyx_t_5numpy_uint_t;
+
+typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
+
+typedef npy_double __pyx_t_5numpy_float_t;
+
+typedef npy_double __pyx_t_5numpy_double_t;
+
+typedef npy_longdouble __pyx_t_5numpy_longdouble_t;
+
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ typedef ::std::complex< float > __pyx_t_float_complex;
+ #else
+ typedef float _Complex __pyx_t_float_complex;
+ #endif
+#else
+ typedef struct { float real, imag; } __pyx_t_float_complex;
+#endif
+
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ typedef ::std::complex< double > __pyx_t_double_complex;
+ #else
+ typedef double _Complex __pyx_t_double_complex;
+ #endif
+#else
+ typedef struct { double real, imag; } __pyx_t_double_complex;
+#endif
+
+/* Type declarations */
+
+typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
+
+typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
+
+typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
+
+typedef npy_cdouble __pyx_t_5numpy_complex_t;
+
+#ifndef CYTHON_REFNANNY
+ #define CYTHON_REFNANNY 0
+#endif
+
+#if CYTHON_REFNANNY
+ typedef struct {
+ void (*INCREF)(void*, PyObject*, int);
+ void (*DECREF)(void*, PyObject*, int);
+ void (*GOTREF)(void*, PyObject*, int);
+ void (*GIVEREF)(void*, PyObject*, int);
+ void* (*SetupContext)(const char*, int, const char*);
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+ m = PyImport_ImportModule((char *)modname);
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+ p = PyObject_GetAttrString(m, (char *)"RefNannyAPI");
+ if (!p) goto end;
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+ end:
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+ #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+ #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
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-#define __Pyx_INCREF(r) Py_INCREF(r)
-#define __Pyx_DECREF(r) Py_DECREF(r)
-#define __Pyx_GOTREF(r)
-#define __Pyx_GIVEREF(r)
-#define __Pyx_XDECREF(r) Py_XDECREF(r)
-#define __Pyx_SetupRefcountContext(name)
-#define __Pyx_FinishRefcountContext()
+ #define __Pyx_RefNannySetupContext(name)
+ #define __Pyx_RefNannyFinishContext()
+ #define __Pyx_INCREF(r) Py_INCREF(r)
+ #define __Pyx_DECREF(r) Py_DECREF(r)
+ #define __Pyx_GOTREF(r)
+ #define __Pyx_GIVEREF(r)
+ #define __Pyx_XDECREF(r) Py_XDECREF(r)
#endif /* CYTHON_REFNANNY */
-#define __Pyx_XGIVEREF(r) if((r) == NULL) ; else __Pyx_GIVEREF(r)
-#define __Pyx_XGOTREF(r) if((r) == NULL) ; else __Pyx_GOTREF(r)
+#define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);} } while(0)
+#define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r);} } while(0)
static void __Pyx_RaiseDoubleKeywordsError(
const char* func_name, PyObject* kw_name); /*proto*/
@@ -294,86 +435,25 @@ typedef struct {
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static int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack);
-static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
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static void __Pyx_RaiseBufferFallbackError(void); /*proto*/
static INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
static INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
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- return r;
- }
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- return __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i));
-}
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+static int __Pyx_EndUnpack(PyObject *); /*proto*/
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- __Pyx_GetItemInt_Fast(o, i, size <= sizeof(long)) : \
- __Pyx_GetItemInt_Generic(o, to_py_func(i)))
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-static INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int fits_long) {
- PyObject *r;
- if (PyList_CheckExact(o) && ((0 <= i) & (i < PyList_GET_SIZE(o)))) {
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static int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
const char *name, int exact); /*proto*/
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static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
-#if __PYX_USE_C99_COMPLEX
- #define __Pyx_REAL_PART(z) __real__(z)
- #define __Pyx_IMAG_PART(z) __imag__(z)
+static INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp);
+
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ #define __Pyx_CREAL(z) ((z).real())
+ #define __Pyx_CIMAG(z) ((z).imag())
+ #else
+ #define __Pyx_CREAL(z) (__real__(z))
+ #define __Pyx_CIMAG(z) (__imag__(z))
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- #define __Pyx_REAL_PART(z) ((z).real)
- #define __Pyx_IMAG_PART(z) ((z).imag)
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+ #define __Pyx_CIMAG(z) ((z).imag)
#endif
-#define __pyx_PyObject_from_complex(z) PyComplex_FromDoubles((double)__Pyx_REAL_PART(z), (double)__Pyx_IMAG_PART(z))
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-#if __PYX_USE_C99_COMPLEX
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- #define __pyx_t_float_complex_is_zero(a) ((a) == 0)
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- #define __pyx_t_float_complex_sub(a, b) ((a)-(b))
- #define __pyx_t_float_complex_mul(a, b) ((a)*(b))
- #define __pyx_t_float_complex_div(a, b) ((a)/(b))
- #define __pyx_t_float_complex_neg(a) (-(a))
-
+#if defined(_WIN32) && defined(__cplusplus) && CYTHON_CCOMPLEX
+ #define __Pyx_SET_CREAL(z,x) ((z).real(x))
+ #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
#else
-
- typedef struct { float real, imag; } __pyx_t_float_complex;
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
- __pyx_t_float_complex c; c.real = x; c.imag = y; return c;
- }
-
- static INLINE int __pyx_t_float_complex_is_zero(__pyx_t_float_complex a) {
- return (a.real == 0) & (a.imag == 0);
- }
-
- static INLINE int __pyx_t_float_complex_eq(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- return (a.real == b.real) & (a.imag == b.imag);
- }
-
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_add(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- __pyx_t_float_complex z;
- z.real = a.real + b.real;
- z.imag = a.imag + b.imag;
- return z;
- }
-
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_sub(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- __pyx_t_float_complex z;
- z.real = a.real - b.real;
- z.imag = a.imag - b.imag;
- return z;
- }
-
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_mul(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- __pyx_t_float_complex z;
- z.real = a.real * b.real - a.imag * b.imag;
- z.imag = a.real * b.imag + a.imag * b.real;
- return z;
- }
-
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_div(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- __pyx_t_float_complex z;
- float denom = b.real*b.real + b.imag*b.imag;
- z.real = (a.real * b.real + a.imag * b.imag) / denom;
- z.imag = (a.imag * b.real - a.real * b.imag) / denom;
- return z;
- }
-
- static INLINE __pyx_t_float_complex __pyx_t_float_complex_neg(__pyx_t_float_complex a) {
- __pyx_t_float_complex z;
- z.real = -a.real;
- z.imag = -a.imag;
- return z;
- }
-
+ #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
+ #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
#endif
-#if __PYX_USE_C99_COMPLEX
-
- typedef double _Complex __pyx_t_double_complex;
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
- return x + y*(__pyx_t_double_complex)_Complex_I;
- }
-
- #define __pyx_t_double_complex_is_zero(a) ((a) == 0)
- #define __pyx_t_double_complex_eq(a, b) ((a) == (b))
- #define __pyx_t_double_complex_add(a, b) ((a)+(b))
- #define __pyx_t_double_complex_sub(a, b) ((a)-(b))
- #define __pyx_t_double_complex_mul(a, b) ((a)*(b))
- #define __pyx_t_double_complex_div(a, b) ((a)/(b))
- #define __pyx_t_double_complex_neg(a) (-(a))
-
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);
+ #else
+ static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);
+ #endif
#else
-
- typedef struct { double real, imag; } __pyx_t_double_complex;
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
- __pyx_t_double_complex c; c.real = x; c.imag = y; return c;
- }
-
- static INLINE int __pyx_t_double_complex_is_zero(__pyx_t_double_complex a) {
- return (a.real == 0) & (a.imag == 0);
- }
-
- static INLINE int __pyx_t_double_complex_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- return (a.real == b.real) & (a.imag == b.imag);
- }
-
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_add(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- __pyx_t_double_complex z;
- z.real = a.real + b.real;
- z.imag = a.imag + b.imag;
- return z;
- }
-
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_sub(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- __pyx_t_double_complex z;
- z.real = a.real - b.real;
- z.imag = a.imag - b.imag;
- return z;
- }
-
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_mul(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- __pyx_t_double_complex z;
- z.real = a.real * b.real - a.imag * b.imag;
- z.imag = a.real * b.imag + a.imag * b.real;
- return z;
- }
-
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_div(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- __pyx_t_double_complex z;
- double denom = b.real*b.real + b.imag*b.imag;
- z.real = (a.real * b.real + a.imag * b.imag) / denom;
- z.imag = (a.imag * b.real - a.real * b.imag) / denom;
- return z;
- }
-
- static INLINE __pyx_t_double_complex __pyx_t_double_complex_neg(__pyx_t_double_complex a) {
- __pyx_t_double_complex z;
- z.real = -a.real;
- z.imag = -a.imag;
- return z;
- }
-
+ static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);
#endif
-#if __PYX_USE_C99_COMPLEX
-
- typedef long double _Complex __pyx_t_long__double_complex;
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_from_parts(long double x, long double y) {
- return x + y*(__pyx_t_long__double_complex)_Complex_I;
- }
-
- #define __pyx_t_long__double_complex_is_zero(a) ((a) == 0)
- #define __pyx_t_long__double_complex_eq(a, b) ((a) == (b))
- #define __pyx_t_long__double_complex_add(a, b) ((a)+(b))
- #define __pyx_t_long__double_complex_sub(a, b) ((a)-(b))
- #define __pyx_t_long__double_complex_mul(a, b) ((a)*(b))
- #define __pyx_t_long__double_complex_div(a, b) ((a)/(b))
- #define __pyx_t_long__double_complex_neg(a) (-(a))
-
+#if CYTHON_CCOMPLEX
+ #define __Pyx_c_eqf(a, b) ((a)==(b))
+ #define __Pyx_c_sumf(a, b) ((a)+(b))
+ #define __Pyx_c_difff(a, b) ((a)-(b))
+ #define __Pyx_c_prodf(a, b) ((a)*(b))
+ #define __Pyx_c_quotf(a, b) ((a)/(b))
+ #define __Pyx_c_negf(a) (-(a))
+ #ifdef __cplusplus
+ #define __Pyx_c_is_zerof(z) ((z)==(float)0)
+ #define __Pyx_c_conjf(z) (::std::conj(z))
+ /*#define __Pyx_c_absf(z) (::std::abs(z))*/
+ #else
+ #define __Pyx_c_is_zerof(z) ((z)==0)
+ #define __Pyx_c_conjf(z) (conjf(z))
+ /*#define __Pyx_c_absf(z) (cabsf(z))*/
+ #endif
#else
+ static INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex);
+ static INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex);
+ static INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex);
+ static INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex);
+ static INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex);
+ static INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex);
+ static INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex);
+ static INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex);
+ /*static INLINE float __Pyx_c_absf(__pyx_t_float_complex);*/
+#endif
- typedef struct { long double real, imag; } __pyx_t_long__double_complex;
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_from_parts(long double x, long double y) {
- __pyx_t_long__double_complex c; c.real = x; c.imag = y; return c;
- }
-
- static INLINE int __pyx_t_long__double_complex_is_zero(__pyx_t_long__double_complex a) {
- return (a.real == 0) & (a.imag == 0);
- }
-
- static INLINE int __pyx_t_long__double_complex_eq(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
- return (a.real == b.real) & (a.imag == b.imag);
- }
-
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_add(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
- __pyx_t_long__double_complex z;
- z.real = a.real + b.real;
- z.imag = a.imag + b.imag;
- return z;
- }
-
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_sub(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
- __pyx_t_long__double_complex z;
- z.real = a.real - b.real;
- z.imag = a.imag - b.imag;
- return z;
- }
-
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_mul(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
- __pyx_t_long__double_complex z;
- z.real = a.real * b.real - a.imag * b.imag;
- z.imag = a.real * b.imag + a.imag * b.real;
- return z;
- }
-
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_div(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
- __pyx_t_long__double_complex z;
- long double denom = b.real*b.real + b.imag*b.imag;
- z.real = (a.real * b.real + a.imag * b.imag) / denom;
- z.imag = (a.imag * b.real - a.real * b.imag) / denom;
- return z;
- }
-
- static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_neg(__pyx_t_long__double_complex a) {
- __pyx_t_long__double_complex z;
- z.real = -a.real;
- z.imag = -a.imag;
- return z;
- }
-
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
+ #else
+ static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
+ #endif
+#else
+ static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
#endif
-static INLINE int __Pyx_StrEq(const char *, const char *); /*proto*/
+#if CYTHON_CCOMPLEX
+ #define __Pyx_c_eq(a, b) ((a)==(b))
+ #define __Pyx_c_sum(a, b) ((a)+(b))
+ #define __Pyx_c_diff(a, b) ((a)-(b))
+ #define __Pyx_c_prod(a, b) ((a)*(b))
+ #define __Pyx_c_quot(a, b) ((a)/(b))
+ #define __Pyx_c_neg(a) (-(a))
+ #ifdef __cplusplus
+ #define __Pyx_c_is_zero(z) ((z)==(double)0)
+ #define __Pyx_c_conj(z) (::std::conj(z))
+ /*#define __Pyx_c_abs(z) (::std::abs(z))*/
+ #else
+ #define __Pyx_c_is_zero(z) ((z)==0)
+ #define __Pyx_c_conj(z) (conj(z))
+ /*#define __Pyx_c_abs(z) (cabs(z))*/
+ #endif
+#else
+ static INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex);
+ static INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex);
+ static INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex);
+ static INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex);
+ static INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex);
+ static INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex);
+ static INLINE int __Pyx_c_is_zero(__pyx_t_double_complex);
+ static INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex);
+ /*static INLINE double __Pyx_c_abs(__pyx_t_double_complex);*/
+#endif
static INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject *);
@@ -647,65 +610,31 @@ static PyObject *__Pyx_ImportModule(const char *name); /*proto*/
static void __Pyx_AddTraceback(const char *funcname); /*proto*/
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
-
-/* Type declarations */
-
-typedef npy_int8 __pyx_t_5numpy_int8_t;
-
-typedef npy_int16 __pyx_t_5numpy_int16_t;
-
-typedef npy_int32 __pyx_t_5numpy_int32_t;
-
-typedef npy_int64 __pyx_t_5numpy_int64_t;
-
-typedef npy_uint8 __pyx_t_5numpy_uint8_t;
-
-typedef npy_uint16 __pyx_t_5numpy_uint16_t;
-
-typedef npy_uint32 __pyx_t_5numpy_uint32_t;
-
-typedef npy_uint64 __pyx_t_5numpy_uint64_t;
-
-typedef npy_float32 __pyx_t_5numpy_float32_t;
-
-typedef npy_float64 __pyx_t_5numpy_float64_t;
-
-typedef npy_complex64 __pyx_t_5numpy_complex64_t;
-
-typedef npy_complex128 __pyx_t_5numpy_complex128_t;
-
-typedef npy_long __pyx_t_5numpy_int_t;
-
-typedef npy_longlong __pyx_t_5numpy_long_t;
-
-typedef npy_ulong __pyx_t_5numpy_uint_t;
-
-typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
-
-typedef npy_double __pyx_t_5numpy_float_t;
-
-typedef npy_double __pyx_t_5numpy_double_t;
-
-typedef npy_longdouble __pyx_t_5numpy_longdouble_t;
-
-typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
-
-typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
-
-typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
-
-typedef npy_cdouble __pyx_t_5numpy_complex_t;
/* Module declarations from python_buffer */
+/* Module declarations from python_ref */
+
/* Module declarations from stdlib */
+/* Module declarations from stdio */
+
/* Module declarations from numpy */
/* Module declarations from numpy */
static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
+static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
+static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
+static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;
+static INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *); /*proto*/
+static INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *, PyObject *); /*proto*/
+static INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *, PyObject *, PyObject *); /*proto*/
+static INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *, PyObject *, PyObject *, PyObject *); /*proto*/
+static INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *, PyObject *, PyObject *, PyObject *, PyObject *); /*proto*/
static INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/
+static INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *, PyObject *); /*proto*/
+static INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *); /*proto*/
/* Module declarations from libsvm */
static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float64_t = { "numpy.float64_t", NULL, sizeof(__pyx_t_5numpy_float64_t), 'R' };
@@ -715,143 +644,172 @@ static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float_t = { "numpy.float_
int __pyx_module_is_main_libsvm = 0;
/* Implementation of libsvm */
-static char __pyx_k___main__[] = "__main__";
-static PyObject *__pyx_kp___main__;
-static char __pyx_k_X[] = "X";
-static PyObject *__pyx_kp_X;
-static char __pyx_k_Y[] = "Y";
-static PyObject *__pyx_kp_Y;
-static char __pyx_k_svm_type[] = "svm_type";
-static PyObject *__pyx_kp_svm_type;
-static char __pyx_k_kernel_type[] = "kernel_type";
-static PyObject *__pyx_kp_kernel_type;
-static char __pyx_k_degree[] = "degree";
-static PyObject *__pyx_kp_degree;
-static char __pyx_k_gamma[] = "gamma";
-static PyObject *__pyx_kp_gamma;
-static char __pyx_k_coef0[] = "coef0";
-static PyObject *__pyx_kp_coef0;
-static char __pyx_k_eps[] = "eps";
-static PyObject *__pyx_kp_eps;
-static char __pyx_k_C[] = "C";
-static PyObject *__pyx_kp_C;
-static char __pyx_k_nr_weight[] = "nr_weight";
-static PyObject *__pyx_kp_nr_weight;
-static char __pyx_k_weight_label[] = "weight_label";
-static PyObject *__pyx_kp_weight_label;
-static char __pyx_k_weight[] = "weight";
-static PyObject *__pyx_kp_weight;
-static char __pyx_k_nu[] = "nu";
-static PyObject *__pyx_kp_nu;
-static char __pyx_k_cache_size[] = "cache_size";
-static PyObject *__pyx_kp_cache_size;
-static char __pyx_k_p[] = "p";
-static PyObject *__pyx_kp_p;
-static char __pyx_k_shrinking[] = "shrinking";
-static PyObject *__pyx_kp_shrinking;
-static char __pyx_k_probability[] = "probability";
-static PyObject *__pyx_kp_probability;
-static char __pyx_k_T[] = "T";
-static PyObject *__pyx_kp_T;
-static char __pyx_k_SV[] = "SV";
-static PyObject *__pyx_kp_SV;
-static char __pyx_k_sv_coef[] = "sv_coef";
-static PyObject *__pyx_kp_sv_coef;
-static char __pyx_k_rho[] = "rho";
-static PyObject *__pyx_kp_rho;
-static char __pyx_k_nr_class[] = "nr_class";
-static PyObject *__pyx_kp_nr_class;
-static char __pyx_k_nSV[] = "nSV";
-static PyObject *__pyx_kp_nSV;
-static char __pyx_k_label[] = "label";
-static PyObject *__pyx_kp_label;
-static char __pyx_k_probA[] = "probA";
-static PyObject *__pyx_kp_probA;
-static char __pyx_k_probB[] = "probB";
-static PyObject *__pyx_kp_probB;
-static char __pyx_k_numpy[] = "numpy";
-static PyObject *__pyx_kp_numpy;
-static char __pyx_k_np[] = "np";
-static PyObject *__pyx_kp_np;
-static char __pyx_k_MemoryError[] = "MemoryError";
-static PyObject *__pyx_kp_MemoryError;
-static char __pyx_k_ValueError[] = "ValueError";
-static PyObject *__pyx_kp_ValueError;
-static char __pyx_k_empty[] = "empty";
-static PyObject *__pyx_kp_empty;
-static char __pyx_k_zeros[] = "zeros";
-static PyObject *__pyx_kp_zeros;
-static char __pyx_k_dtype[] = "dtype";
-static PyObject *__pyx_kp_dtype;
-static char __pyx_k_66[] = "int32";
-static PyObject *__pyx_kp_66;
-static char __pyx_k_67[] = "float64";
-static PyObject *__pyx_kp_67;
static PyObject *__pyx_builtin_MemoryError;
static PyObject *__pyx_builtin_ValueError;
-static PyObject *__pyx_kp_65;
-static char __pyx_k_65[] = "Seems we've run out of of memory";
-static PyObject *__pyx_kp_68;
-static char __pyx_k_68[] = "We've run out of of memory";
-static PyObject *__pyx_kp_69;
-static char __pyx_k_69[] = "We've run out of of memory";
-static PyObject *__pyx_int_15;
-static char __pyx_k___getbuffer__[] = "__getbuffer__";
-static PyObject *__pyx_kp___getbuffer__;
-static char __pyx_k___releasebuffer__[] = "__releasebuffer__";
-static PyObject *__pyx_kp___releasebuffer__;
-static char __pyx_k_info[] = "info";
-static PyObject *__pyx_kp_info;
-static char __pyx_k_flags[] = "flags";
-static PyObject *__pyx_kp_flags;
-static char __pyx_k_range[] = "range";
-static PyObject *__pyx_kp_range;
-static char __pyx_k_itervalues[] = "itervalues";
-static PyObject *__pyx_kp_itervalues;
-static char __pyx_k_RuntimeError[] = "RuntimeError";
-static PyObject *__pyx_kp_RuntimeError;
-static PyObject *__pyx_kp_35;
-static PyObject *__pyx_kp_36;
-static PyObject *__pyx_kp_39;
-static PyObject *__pyx_kp_57;
static PyObject *__pyx_builtin_range;
static PyObject *__pyx_builtin_RuntimeError;
-static char __pyx_k_35[] = "ndarray is not C contiguous";
-static char __pyx_k_36[] = "ndarray is not Fortran contiguous";
-static char __pyx_k_37[] = ">";
-static char __pyx_k_38[] = "<";
-static char __pyx_k_39[] = "Non-native byte order not supported";
-static char __pyx_k_40[] = "b";
-static char __pyx_k_41[] = "B";
-static char __pyx_k_42[] = "h";
-static char __pyx_k_43[] = "H";
-static char __pyx_k_44[] = "i";
-static char __pyx_k_45[] = "I";
-static char __pyx_k_46[] = "l";
-static char __pyx_k_47[] = "L";
-static char __pyx_k_48[] = "q";
-static char __pyx_k_49[] = "Q";
-static char __pyx_k_50[] = "f";
-static char __pyx_k_51[] = "d";
-static char __pyx_k_52[] = "g";
-static char __pyx_k_53[] = "Zf";
-static char __pyx_k_54[] = "Zd";
-static char __pyx_k_55[] = "Zg";
-static char __pyx_k_56[] = "O";
-static char __pyx_k_57[] = "unknown dtype code in numpy.pxd (%d)";
-static char __pyx_k_58[] = "^";
-static PyObject *__pyx_kp_59;
-static PyObject *__pyx_kp_62;
-static PyObject *__pyx_kp_63;
-static PyObject *__pyx_kp_64;
-static char __pyx_k_59[] = "Format string allocated too short, see comment in numpy.pxd";
-static char __pyx_k_60[] = ">";
-static char __pyx_k_61[] = "<";
-static char __pyx_k_62[] = "Non-native byte order not supported";
-static char __pyx_k_63[] = "Format string allocated too short.";
-static char __pyx_k_64[] = "unknown dtype code in numpy.pxd (%d)";
-
-/* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":85
+static char __pyx_k_1[] = "Seems we've run out of of memory";
+static char __pyx_k_2[] = "We've run out of of memory";
+static char __pyx_k_3[] = "ndarray is not C contiguous";
+static char __pyx_k_4[] = "ndarray is not Fortran contiguous";
+static char __pyx_k_5[] = "Non-native byte order not supported";
+static char __pyx_k_6[] = "unknown dtype code in numpy.pxd (%d)";
+static char __pyx_k_7[] = "Format string allocated too short, see comment in numpy.pxd";
+static char __pyx_k_8[] = "Format string allocated too short.";
+static char __pyx_k_9[] = "\nBinding for libsvm[1]\n---------------------\nWe do not use the binding that ships with libsvm because we need to\naccess svm_model.sv_coeff (and other fields), but libsvm does not\nprovide an accessor. Our solution is to export svm_model and access it\nmanually, this is done un function see svm_train_wrap.\n\nlibsvm uses an sparse representation for the training vectors. In\nmethod dense_to_sparse we translate a dense matrix representation as\nthose produced by NumPy to a sparse representation that libsvm can\nunderstand.\n\nWe define arrays to be the same type as those in libsvm, usually of \ntype C double and C int.\n\nLow-level memory management is done in libsvm_helper.c. If we happen\nto run out of memory a MemoryError will be raised. In practice this is\nnot very helpful since hight changes are malloc fails inside svm.cpp,\nwhere no sort of memory checks are done.\n\nThese are low-level routines, not meant to be used directly. See\nscikits.learn.svm for a higher-level API.\n\n[1] http://www.csie.ntu.edu.tw/~cjlin/libsvm/\n\nNotes\n-----\nMaybe we could speed it a bit further by decorating functions with\n@cython.boundscheck(False), but probably it is not worth since all\nwork is done in lisvm_helper.c\nAlso, the signature mode='c' is somewhat superficial, since we already\ncheck that arrays are C-contiguous in svm.py\n\nAuthors\n-------\n2010: Fabian Pedregosa <fabian.pedregosa@inria.fr>\n Gael Varoquaux <gael.varoquaux@normalesup.org>\n";
+static char __pyx_k_10[] = "train_wrap (line 85)";
+static char __pyx_k_11[] = "predict_from_model_wrap (line 197)";
+static char __pyx_k_12[] = "predict_from_model_wrap";
+static char __pyx_k_13[] = "predict_prob_from_model_wrap (line 260)";
+static char __pyx_k_14[] = "predict_prob_from_model_wrap";
+static char __pyx_k__B[] = "B";
+static char __pyx_k__C[] = "C";
+static char __pyx_k__H[] = "H";
+static char __pyx_k__I[] = "I";
+static char __pyx_k__L[] = "L";
+static char __pyx_k__O[] = "O";
+static char __pyx_k__Q[] = "Q";
+static char __pyx_k__T[] = "T";
+static char __pyx_k__X[] = "X";
+static char __pyx_k__Y[] = "Y";
+static char __pyx_k__b[] = "b";
+static char __pyx_k__d[] = "d";
+static char __pyx_k__f[] = "f";
+static char __pyx_k__g[] = "g";
+static char __pyx_k__h[] = "h";
+static char __pyx_k__i[] = "i";
+static char __pyx_k__l[] = "l";
+static char __pyx_k__p[] = "p";
+static char __pyx_k__q[] = "q";
+static char __pyx_k__SV[] = "SV";
+static char __pyx_k__Zd[] = "Zd";
+static char __pyx_k__Zf[] = "Zf";
+static char __pyx_k__Zg[] = "Zg";
+static char __pyx_k__np[] = "np";
+static char __pyx_k__nu[] = "nu";
+static char __pyx_k__buf[] = "buf";
+static char __pyx_k__eps[] = "eps";
+static char __pyx_k__nSV[] = "nSV";
+static char __pyx_k__obj[] = "obj";
+static char __pyx_k__base[] = "base";
+static char __pyx_k__data[] = "data";
+static char __pyx_k__ndim[] = "ndim";
+static char __pyx_k__coef0[] = "coef0";
+static char __pyx_k__descr[] = "descr";
+static char __pyx_k__dtype[] = "dtype";
+static char __pyx_k__empty[] = "empty";
+static char __pyx_k__gamma[] = "gamma";
+static char __pyx_k__int32[] = "int32";
+static char __pyx_k__label[] = "label";
+static char __pyx_k__names[] = "names";
+static char __pyx_k__numpy[] = "numpy";
+static char __pyx_k__probA[] = "probA";
+static char __pyx_k__probB[] = "probB";
+static char __pyx_k__range[] = "range";
+static char __pyx_k__shape[] = "shape";
+static char __pyx_k__degree[] = "degree";
+static char __pyx_k__fields[] = "fields";
+static char __pyx_k__format[] = "format";
+static char __pyx_k__resize[] = "resize";
+static char __pyx_k__weight[] = "weight";
+static char __pyx_k__float64[] = "float64";
+static char __pyx_k__strides[] = "strides";
+static char __pyx_k__sv_coef[] = "sv_coef";
+static char __pyx_k____main__[] = "__main__";
+static char __pyx_k____test__[] = "__test__";
+static char __pyx_k__itemsize[] = "itemsize";
+static char __pyx_k__readonly[] = "readonly";
+static char __pyx_k__refcheck[] = "refcheck";
+static char __pyx_k__svm_type[] = "svm_type";
+static char __pyx_k__type_num[] = "type_num";
+static char __pyx_k__byteorder[] = "byteorder";
+static char __pyx_k__intercept[] = "intercept";
+static char __pyx_k__nclass_SV[] = "nclass_SV";
+static char __pyx_k__nr_weight[] = "nr_weight";
+static char __pyx_k__shrinking[] = "shrinking";
+static char __pyx_k__ValueError[] = "ValueError";
+static char __pyx_k__cache_size[] = "cache_size";
+static char __pyx_k__suboffsets[] = "suboffsets";
+static char __pyx_k__train_wrap[] = "train_wrap";
+static char __pyx_k__MemoryError[] = "MemoryError";
+static char __pyx_k__kernel_type[] = "kernel_type";
+static char __pyx_k__probability[] = "probability";
+static char __pyx_k__RuntimeError[] = "RuntimeError";
+static char __pyx_k__weight_label[] = "weight_label";
+static PyObject *__pyx_kp_s_1;
+static PyObject *__pyx_kp_u_10;
+static PyObject *__pyx_kp_u_11;
+static PyObject *__pyx_n_s_12;
+static PyObject *__pyx_kp_u_13;
+static PyObject *__pyx_n_s_14;
+static PyObject *__pyx_kp_s_2;
+static PyObject *__pyx_kp_u_3;
+static PyObject *__pyx_kp_u_4;
+static PyObject *__pyx_kp_u_5;
+static PyObject *__pyx_kp_u_6;
+static PyObject *__pyx_kp_u_7;
+static PyObject *__pyx_kp_u_8;
+static PyObject *__pyx_n_s__C;
+static PyObject *__pyx_n_s__MemoryError;
+static PyObject *__pyx_n_s__RuntimeError;
+static PyObject *__pyx_n_s__SV;
+static PyObject *__pyx_n_s__T;
+static PyObject *__pyx_n_s__ValueError;
+static PyObject *__pyx_n_s__X;
+static PyObject *__pyx_n_s__Y;
+static PyObject *__pyx_n_s____main__;
+static PyObject *__pyx_n_s____test__;
+static PyObject *__pyx_n_s__base;
+static PyObject *__pyx_n_s__buf;
+static PyObject *__pyx_n_s__byteorder;
+static PyObject *__pyx_n_s__cache_size;
+static PyObject *__pyx_n_s__coef0;
+static PyObject *__pyx_n_s__data;
+static PyObject *__pyx_n_s__degree;
+static PyObject *__pyx_n_s__descr;
+static PyObject *__pyx_n_s__dtype;
+static PyObject *__pyx_n_s__empty;
+static PyObject *__pyx_n_s__eps;
+static PyObject *__pyx_n_s__fields;
+static PyObject *__pyx_n_s__float64;
+static PyObject *__pyx_n_s__format;
+static PyObject *__pyx_n_s__gamma;
+static PyObject *__pyx_n_s__int32;
+static PyObject *__pyx_n_s__intercept;
+static PyObject *__pyx_n_s__itemsize;
+static PyObject *__pyx_n_s__kernel_type;
+static PyObject *__pyx_n_s__label;
+static PyObject *__pyx_n_s__nSV;
+static PyObject *__pyx_n_s__names;
+static PyObject *__pyx_n_s__nclass_SV;
+static PyObject *__pyx_n_s__ndim;
+static PyObject *__pyx_n_s__np;
+static PyObject *__pyx_n_s__nr_weight;
+static PyObject *__pyx_n_s__nu;
+static PyObject *__pyx_n_s__numpy;
+static PyObject *__pyx_n_s__obj;
+static PyObject *__pyx_n_s__p;
+static PyObject *__pyx_n_s__probA;
+static PyObject *__pyx_n_s__probB;
+static PyObject *__pyx_n_s__probability;
+static PyObject *__pyx_n_s__range;
+static PyObject *__pyx_n_s__readonly;
+static PyObject *__pyx_n_s__refcheck;
+static PyObject *__pyx_n_s__resize;
+static PyObject *__pyx_n_s__shape;
+static PyObject *__pyx_n_s__shrinking;
+static PyObject *__pyx_n_s__strides;
+static PyObject *__pyx_n_s__suboffsets;
+static PyObject *__pyx_n_s__sv_coef;
+static PyObject *__pyx_n_s__svm_type;
+static PyObject *__pyx_n_s__train_wrap;
+static PyObject *__pyx_n_s__type_num;
+static PyObject *__pyx_n_s__weight;
+static PyObject *__pyx_n_s__weight_label;
+static PyObject *__pyx_int_0;
+static PyObject *__pyx_int_15;
+
+/* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":85
* # Wrapper functions
*
* def train_wrap ( np.ndarray[np.float64_t, ndim=2, mode='c'] X, # <<<<<<<<<<<<<<
@@ -860,7 +818,7 @@ static char __pyx_k_64[] = "unknown dtype code in numpy.pxd (%d)";
*/
static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6libsvm_train_wrap[] = "\n Wrapper for svm_train in libsvm\n\n Parameters\n ----------\n X: array-like, dtype=float, size=[N, D]\n\n Y: array, dtype=float, size=[N]\n target vector\n\n Optional Parameters\n -------------------\n See scikits.learn.svm.predict for a complete list of parameters.\n\n Return\n ------\n sv_coef: array of coeficients for support vector in decision\n function (aka alphas)\n rho : array\n constants in decision functions\n SV : array-like\n support vectors\n TODO\n ";
+static char __pyx_doc_6libsvm_train_wrap[] = "\n Wrap svm_train from libsvm\n\n Parameters\n ----------\n X: array-like, dtype=float, size=[N, D]\n\n Y: array, dtype=float, size=[N]\n target vector\n\n ...\n\n Notes\n -------------------\n See scikits.learn.svm.predict for a complete list of parameters.\n\n Return\n ------\n sv_coef: array of coeficients for support vector in decision\n function (aka alphas)\n intercept : array\n constants in decision functions\n SV : array-like\n support vectors\n TODO\n ";
static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
PyArrayObject *__pyx_v_X = 0;
PyArrayObject *__pyx_v_Y = 0;
@@ -872,8 +830,12 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
double __pyx_v_eps;
double __pyx_v_C;
int __pyx_v_nr_weight;
+ PyArrayObject *__pyx_v_SV = 0;
+ PyArrayObject *__pyx_v_sv_coef = 0;
+ PyArrayObject *__pyx_v_intercept = 0;
PyArrayObject *__pyx_v_weight_label = 0;
PyArrayObject *__pyx_v_weight = 0;
+ PyArrayObject *__pyx_v_nclass_SV = 0;
double __pyx_v_nu;
double __pyx_v_cache_size;
double __pyx_v_p;
@@ -883,12 +845,8 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
struct svm_problem *__pyx_v_problem;
struct svm_model *__pyx_v_model;
char *__pyx_v_error_msg;
- int __pyx_v_nSV;
+ int __pyx_v_SV_len;
int __pyx_v_nr;
- PyArrayObject *__pyx_v_sv_coef;
- PyArrayObject *__pyx_v_rho;
- PyArrayObject *__pyx_v_SV;
- PyArrayObject *__pyx_v_nclass_SV;
PyArrayObject *__pyx_v_label;
PyArrayObject *__pyx_v_probA;
PyArrayObject *__pyx_v_probB;
@@ -898,28 +856,20 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
Py_buffer __pyx_bstruct_weight_label;
Py_ssize_t __pyx_bstride_0_weight_label = 0;
Py_ssize_t __pyx_bshape_0_weight_label = 0;
- Py_buffer __pyx_bstruct_probA;
- Py_ssize_t __pyx_bstride_0_probA = 0;
- Py_ssize_t __pyx_bshape_0_probA = 0;
- Py_buffer __pyx_bstruct_probB;
- Py_ssize_t __pyx_bstride_0_probB = 0;
- Py_ssize_t __pyx_bshape_0_probB = 0;
- Py_buffer __pyx_bstruct_label;
- Py_ssize_t __pyx_bstride_0_label = 0;
- Py_ssize_t __pyx_bshape_0_label = 0;
- Py_buffer __pyx_bstruct_sv_coef;
- Py_ssize_t __pyx_bstride_0_sv_coef = 0;
- Py_ssize_t __pyx_bstride_1_sv_coef = 0;
- Py_ssize_t __pyx_bshape_0_sv_coef = 0;
- Py_ssize_t __pyx_bshape_1_sv_coef = 0;
- Py_buffer __pyx_bstruct_rho;
- Py_ssize_t __pyx_bstride_0_rho = 0;
- Py_ssize_t __pyx_bshape_0_rho = 0;
Py_buffer __pyx_bstruct_SV;
Py_ssize_t __pyx_bstride_0_SV = 0;
Py_ssize_t __pyx_bstride_1_SV = 0;
Py_ssize_t __pyx_bshape_0_SV = 0;
Py_ssize_t __pyx_bshape_1_SV = 0;
+ Py_buffer __pyx_bstruct_label;
+ Py_ssize_t __pyx_bstride_0_label = 0;
+ Py_ssize_t __pyx_bshape_0_label = 0;
+ Py_buffer __pyx_bstruct_probB;
+ Py_ssize_t __pyx_bstride_0_probB = 0;
+ Py_ssize_t __pyx_bshape_0_probB = 0;
+ Py_buffer __pyx_bstruct_intercept;
+ Py_ssize_t __pyx_bstride_0_intercept = 0;
+ Py_ssize_t __pyx_bshape_0_intercept = 0;
Py_buffer __pyx_bstruct_Y;
Py_ssize_t __pyx_bstride_0_Y = 0;
Py_ssize_t __pyx_bshape_0_Y = 0;
@@ -931,32 +881,41 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
Py_buffer __pyx_bstruct_nclass_SV;
Py_ssize_t __pyx_bstride_0_nclass_SV = 0;
Py_ssize_t __pyx_bshape_0_nclass_SV = 0;
+ Py_buffer __pyx_bstruct_sv_coef;
+ Py_ssize_t __pyx_bstride_0_sv_coef = 0;
+ Py_ssize_t __pyx_bstride_1_sv_coef = 0;
+ Py_ssize_t __pyx_bshape_0_sv_coef = 0;
+ Py_ssize_t __pyx_bshape_1_sv_coef = 0;
+ Py_buffer __pyx_bstruct_probA;
+ Py_ssize_t __pyx_bstride_0_probA = 0;
+ Py_ssize_t __pyx_bshape_0_probA = 0;
PyObject *__pyx_r = NULL;
- PyObject *__pyx_1 = 0;
- PyObject *__pyx_2 = 0;
int __pyx_t_1;
- PyObject *__pyx_t_2 = NULL;
- PyObject *__pyx_t_3 = NULL;
+ int __pyx_t_2;
+ int __pyx_t_3;
PyObject *__pyx_t_4 = NULL;
PyObject *__pyx_t_5 = NULL;
- PyArrayObject *__pyx_t_6 = NULL;
- int __pyx_t_7;
+ PyObject *__pyx_t_6 = NULL;
+ PyObject *__pyx_t_7 = NULL;
PyObject *__pyx_t_8 = NULL;
- PyObject *__pyx_t_9 = NULL;
- PyObject *__pyx_t_10 = NULL;
- PyArrayObject *__pyx_t_11 = NULL;
- PyArrayObject *__pyx_t_12 = NULL;
- PyArrayObject *__pyx_t_13 = NULL;
+ PyArrayObject *__pyx_t_9 = NULL;
+ int __pyx_t_10;
+ PyObject *__pyx_t_11 = NULL;
+ PyObject *__pyx_t_12 = NULL;
+ PyObject *__pyx_t_13 = NULL;
PyArrayObject *__pyx_t_14 = NULL;
PyArrayObject *__pyx_t_15 = NULL;
- PyArrayObject *__pyx_t_16 = NULL;
- static PyObject **__pyx_pyargnames[] = {&__pyx_kp_X,&__pyx_kp_Y,&__pyx_kp_svm_type,&__pyx_kp_kernel_type,&__pyx_kp_degree,&__pyx_kp_gamma,&__pyx_kp_coef0,&__pyx_kp_eps,&__pyx_kp_C,&__pyx_kp_nr_weight,&__pyx_kp_weight_label,&__pyx_kp_weight,&__pyx_kp_nu,&__pyx_kp_cache_size,&__pyx_kp_p,&__pyx_kp_shrinking,&__pyx_kp_probability,0};
- __Pyx_SetupRefcountContext("train_wrap");
+ static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__X,&__pyx_n_s__Y,&__pyx_n_s__svm_type,&__pyx_n_s__kernel_type,&__pyx_n_s__degree,&__pyx_n_s__gamma,&__pyx_n_s__coef0,&__pyx_n_s__eps,&__pyx_n_s__C,&__pyx_n_s__nr_weight,&__pyx_n_s__SV,&__pyx_n_s__sv_coef,&__pyx_n_s__intercept,&__pyx_n_s__weight_label,&__pyx_n_s__weight,&__pyx_n_s__nclass_SV,&__pyx_n_s__nu,&__pyx_n_s__cache_size,&__pyx_n_s__p,&__pyx_n_s__shrinking,&__pyx_n_s__probability,0};
+ __Pyx_RefNannySetupContext("train_wrap");
__pyx_self = __pyx_self;
if (unlikely(__pyx_kwds)) {
Py_ssize_t kw_args = PyDict_Size(__pyx_kwds);
- PyObject* values[17] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
+ PyObject* values[21] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
switch (PyTuple_GET_SIZE(__pyx_args)) {
+ case 21: values[20] = PyTuple_GET_ITEM(__pyx_args, 20);
+ case 20: values[19] = PyTuple_GET_ITEM(__pyx_args, 19);
+ case 19: values[18] = PyTuple_GET_ITEM(__pyx_args, 18);
+ case 18: values[17] = PyTuple_GET_ITEM(__pyx_args, 17);
case 17: values[16] = PyTuple_GET_ITEM(__pyx_args, 16);
case 16: values[15] = PyTuple_GET_ITEM(__pyx_args, 15);
case 15: values[14] = PyTuple_GET_ITEM(__pyx_args, 14);
@@ -979,104 +938,128 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
}
switch (PyTuple_GET_SIZE(__pyx_args)) {
case 0:
- values[0] = PyDict_GetItem(__pyx_kwds, __pyx_kp_X);
+ values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__X);
if (likely(values[0])) kw_args--;
else goto __pyx_L5_argtuple_error;
case 1:
- values[1] = PyDict_GetItem(__pyx_kwds, __pyx_kp_Y);
+ values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__Y);
if (likely(values[1])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 2:
- values[2] = PyDict_GetItem(__pyx_kwds, __pyx_kp_svm_type);
+ values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__svm_type);
if (likely(values[2])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 3:
- values[3] = PyDict_GetItem(__pyx_kwds, __pyx_kp_kernel_type);
+ values[3] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__kernel_type);
if (likely(values[3])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 3); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 3); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 4:
- values[4] = PyDict_GetItem(__pyx_kwds, __pyx_kp_degree);
+ values[4] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__degree);
if (likely(values[4])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 5:
- values[5] = PyDict_GetItem(__pyx_kwds, __pyx_kp_gamma);
+ values[5] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__gamma);
if (likely(values[5])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 5); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 5); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 6:
- values[6] = PyDict_GetItem(__pyx_kwds, __pyx_kp_coef0);
+ values[6] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__coef0);
if (likely(values[6])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 6); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 6); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 7:
- values[7] = PyDict_GetItem(__pyx_kwds, __pyx_kp_eps);
+ values[7] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__eps);
if (likely(values[7])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 7); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 7); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 8:
- values[8] = PyDict_GetItem(__pyx_kwds, __pyx_kp_C);
+ values[8] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__C);
if (likely(values[8])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 8); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 8); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 9:
- values[9] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nr_weight);
+ values[9] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nr_weight);
if (likely(values[9])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 9); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 9); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 10:
- values[10] = PyDict_GetItem(__pyx_kwds, __pyx_kp_weight_label);
+ values[10] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__SV);
if (likely(values[10])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 10); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 10); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 11:
- values[11] = PyDict_GetItem(__pyx_kwds, __pyx_kp_weight);
+ values[11] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__sv_coef);
if (likely(values[11])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 11); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 11); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 12:
- values[12] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nu);
+ values[12] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__intercept);
if (likely(values[12])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 12); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 12); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 13:
- values[13] = PyDict_GetItem(__pyx_kwds, __pyx_kp_cache_size);
+ values[13] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__weight_label);
if (likely(values[13])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 13); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 13); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 14:
- values[14] = PyDict_GetItem(__pyx_kwds, __pyx_kp_p);
+ values[14] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__weight);
if (likely(values[14])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 14); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 14); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 15:
- values[15] = PyDict_GetItem(__pyx_kwds, __pyx_kp_shrinking);
+ values[15] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nclass_SV);
if (likely(values[15])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 15); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 15); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 16:
- values[16] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probability);
+ values[16] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nu);
if (likely(values[16])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 17, 17, 16); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 16); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ }
+ case 17:
+ values[17] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__cache_size);
+ if (likely(values[17])) kw_args--;
+ else {
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 17); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ }
+ case 18:
+ values[18] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__p);
+ if (likely(values[18])) kw_args--;
+ else {
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 18); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ }
+ case 19:
+ values[19] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__shrinking);
+ if (likely(values[19])) kw_args--;
+ else {
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 19); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ }
+ case 20:
+ values[20] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probability);
+ if (likely(values[20])) kw_args--;
+ else {
+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, 20); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
}
if (unlikely(kw_args > 0)) {
@@ -1092,14 +1075,18 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
__pyx_v_eps = __pyx_PyFloat_AsDouble(values[7]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_v_C = __pyx_PyFloat_AsDouble(values[8]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_v_nr_weight = __Pyx_PyInt_AsInt(values[9]); if (unlikely((__pyx_v_nr_weight == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_weight_label = ((PyArrayObject *)values[10]);
- __pyx_v_weight = ((PyArrayObject *)values[11]);
- __pyx_v_nu = __pyx_PyFloat_AsDouble(values[12]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_cache_size = __pyx_PyFloat_AsDouble(values[13]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_p = __pyx_PyFloat_AsDouble(values[14]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_shrinking = __Pyx_PyInt_AsInt(values[15]); if (unlikely((__pyx_v_shrinking == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_probability = __Pyx_PyInt_AsInt(values[16]); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- } else if (PyTuple_GET_SIZE(__pyx_args) != 17) {
+ __pyx_v_SV = ((PyArrayObject *)values[10]);
+ __pyx_v_sv_coef = ((PyArrayObject *)values[11]);
+ __pyx_v_intercept = ((PyArrayObject *)values[12]);
+ __pyx_v_weight_label = ((PyArrayObject *)values[13]);
+ __pyx_v_weight = ((PyArrayObject *)values[14]);
+ __pyx_v_nclass_SV = ((PyArrayObject *)values[15]);
+ __pyx_v_nu = __pyx_PyFloat_AsDouble(values[16]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_cache_size = __pyx_PyFloat_AsDouble(values[17]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_p = __pyx_PyFloat_AsDouble(values[18]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_shrinking = __Pyx_PyInt_AsInt(values[19]); if (unlikely((__pyx_v_shrinking == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_probability = __Pyx_PyInt_AsInt(values[20]); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 96; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ } else if (PyTuple_GET_SIZE(__pyx_args) != 21) {
goto __pyx_L5_argtuple_error;
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@@ -1112,43 +1099,55 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
__pyx_v_eps = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 7)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_v_C = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 8)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_v_nr_weight = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 9)); if (unlikely((__pyx_v_nr_weight == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_weight_label = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 10));
- __pyx_v_weight = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 11));
- __pyx_v_nu = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 12)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_cache_size = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 13)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_p = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 14)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_shrinking = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 15)); if (unlikely((__pyx_v_shrinking == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_probability = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 16)); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_SV = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 10));
+ __pyx_v_sv_coef = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 11));
+ __pyx_v_intercept = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 12));
+ __pyx_v_weight_label = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 13));
+ __pyx_v_weight = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 14));
+ __pyx_v_nclass_SV = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 15));
+ __pyx_v_nu = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 16)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_cache_size = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 17)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __pyx_v_shrinking = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 19)); if (unlikely((__pyx_v_shrinking == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_probability = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 20)); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 96; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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goto __pyx_L4_argument_unpacking_done;
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+ __Pyx_RaiseArgtupleInvalid("train_wrap", 1, 21, 21, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_L3_error:;
__Pyx_AddTraceback("libsvm.train_wrap");
return NULL;
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- __pyx_v_rho = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
- __pyx_v_SV = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
- __pyx_v_nclass_SV = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
+ __Pyx_INCREF((PyObject *)__pyx_v_X);
+ __Pyx_INCREF((PyObject *)__pyx_v_Y);
+ __Pyx_INCREF((PyObject *)__pyx_v_SV);
+ __Pyx_INCREF((PyObject *)__pyx_v_sv_coef);
+ __Pyx_INCREF((PyObject *)__pyx_v_intercept);
+ __Pyx_INCREF((PyObject *)__pyx_v_weight_label);
+ __Pyx_INCREF((PyObject *)__pyx_v_weight);
+ __Pyx_INCREF((PyObject *)__pyx_v_nclass_SV);
__pyx_v_label = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
__pyx_v_probA = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
__pyx_v_probB = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
- __pyx_bstruct_sv_coef.buf = NULL;
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- __pyx_bstruct_nclass_SV.buf = NULL;
__pyx_bstruct_label.buf = NULL;
__pyx_bstruct_probA.buf = NULL;
__pyx_bstruct_probB.buf = NULL;
__pyx_bstruct_X.buf = NULL;
__pyx_bstruct_Y.buf = NULL;
+ __pyx_bstruct_SV.buf = NULL;
+ __pyx_bstruct_sv_coef.buf = NULL;
+ __pyx_bstruct_intercept.buf = NULL;
__pyx_bstruct_weight_label.buf = NULL;
__pyx_bstruct_weight.buf = NULL;
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if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_X), __pyx_ptype_5numpy_ndarray, 1, "X", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_Y), __pyx_ptype_5numpy_ndarray, 1, "Y", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 86; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_weight_label), __pyx_ptype_5numpy_ndarray, 1, "weight_label", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 89; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_weight), __pyx_ptype_5numpy_ndarray, 1, "weight", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_SV), __pyx_ptype_5numpy_ndarray, 1, "SV", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 89; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_intercept), __pyx_ptype_5numpy_ndarray, 1, "intercept", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_weight), __pyx_ptype_5numpy_ndarray, 1, "weight", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 93; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_nclass_SV), __pyx_ptype_5numpy_ndarray, 1, "nclass_SV", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 94; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_BufFmt_StackElem __pyx_stack[1];
if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_bstruct_X, (PyObject*)__pyx_v_X, &__Pyx_TypeInfo_nn___pyx_t_5numpy_float64_t, PyBUF_FORMAT| PyBUF_C_CONTIGUOUS, 2, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
@@ -1163,18 +1162,42 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
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{
__Pyx_BufFmt_StackElem __pyx_stack[1];
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+ }
+ __pyx_bstride_0_SV = __pyx_bstruct_SV.strides[0]; __pyx_bstride_1_SV = __pyx_bstruct_SV.strides[1];
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+ {
+ __Pyx_BufFmt_StackElem __pyx_stack[1];
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+ }
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+ __pyx_bshape_0_sv_coef = __pyx_bstruct_sv_coef.shape[0]; __pyx_bshape_1_sv_coef = __pyx_bstruct_sv_coef.shape[1];
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{
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}
__pyx_bstride_0_weight = __pyx_bstruct_weight.strides[0];
__pyx_bshape_0_weight = __pyx_bstruct_weight.shape[0];
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":124
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":130
*
* # set libsvm problem
* problem = set_problem(X.data, Y.data, X.shape) # <<<<<<<<<<<<<<
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*/
__pyx_v_problem = set_problem(__pyx_v_X->data, __pyx_v_Y->data, __pyx_v_X->dimensions);
- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":130
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":136
* coef0, nu, cache_size,
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@@ -1192,43 +1215,45 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
*/
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":133
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":139
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* raise MemoryError("Seems we've run out of of memory")
* error_msg = svm_check_parameter(problem, param);
*/
- if (!(__pyx_v_param == NULL)) {
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+ __pyx_t_1 = (__pyx_v_param == NULL);
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- __pyx_t_1 = (__pyx_v_param == NULL);
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}
- if (__pyx_t_1) {
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":134
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":140
* # check parameters
* if (param == NULL or problem == NULL):
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*/
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goto __pyx_L6;
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__pyx_L6:;
- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":135
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":141
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@@ -1237,17 +1262,17 @@ static PyObject *__pyx_pf_6libsvm_train_wrap(PyObject *__pyx_self, PyObject *__p
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":136
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":142
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*/
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":137
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":143
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*/
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":138
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*/
free_param(__pyx_v_param);
- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":139
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goto __pyx_L7;
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__pyx_L7:;
- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":142
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":144
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*/
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":145
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":185
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*/
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":186
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PyArrayObject *__pyx_v_SV = 0;
PyArrayObject *__pyx_v_sv_coef = 0;
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int __pyx_v_svm_type;
int __pyx_v_kernel_type;
int __pyx_v_degree;
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int __pyx_v_shrinking;
int __pyx_v_probability;
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PyArrayObject *__pyx_v_label = 0;
PyArrayObject *__pyx_v_probA = 0;
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Py_ssize_t __pyx_bshape_0_sv_coef = 0;
Py_ssize_t __pyx_bshape_1_sv_coef = 0;
+ Py_buffer __pyx_bstruct_intercept;
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}
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 2:
- values[2] = PyDict_GetItem(__pyx_kwds, __pyx_kp_sv_coef);
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 3:
- values[3] = PyDict_GetItem(__pyx_kwds, __pyx_kp_rho);
+ values[3] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__intercept);
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 3); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 5); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 6:
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 6); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 6); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 7:
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 7); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 8:
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 8); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 8); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 9:
- values[9] = PyDict_GetItem(__pyx_kwds, __pyx_kp_eps);
+ values[9] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__eps);
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 9); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 9); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 10:
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+ values[10] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__C);
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else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 10); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 10); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 11:
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+ values[11] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nr_weight);
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 11); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 12:
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 12); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 12); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 13:
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+ values[13] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__weight);
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+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 13); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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case 14:
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+ values[14] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nu);
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else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 14); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 14); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 15:
- values[15] = PyDict_GetItem(__pyx_kwds, __pyx_kp_cache_size);
+ values[15] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__cache_size);
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else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 15); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 15); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 16:
- values[16] = PyDict_GetItem(__pyx_kwds, __pyx_kp_p);
+ values[16] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__p);
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else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 16); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 16); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 17:
- values[17] = PyDict_GetItem(__pyx_kwds, __pyx_kp_shrinking);
+ values[17] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__shrinking);
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else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 17); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 17); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 18:
- values[18] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probability);
+ values[18] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probability);
if (likely(values[18])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 18); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 18); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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case 19:
- values[19] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nr_class);
+ values[19] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nSV);
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 19); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 19); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 20:
- values[20] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nSV);
+ values[20] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__label);
if (likely(values[20])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 20); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 20); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 21:
- values[21] = PyDict_GetItem(__pyx_kwds, __pyx_kp_label);
+ values[21] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probA);
if (likely(values[21])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 21); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 21); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 22:
- values[22] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probA);
+ values[22] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probB);
if (likely(values[22])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 22); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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- case 23:
- values[23] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probB);
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- __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 24, 24, 23); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 191; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_from_model_wrap", 1, 23, 23, 22); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "predict_from_model_wrap") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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- __pyx_v_C = __pyx_PyFloat_AsDouble(values[10]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 197; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __pyx_v_intercept = ((PyArrayObject *)values[3]);
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+ __pyx_v_kernel_type = __Pyx_PyInt_AsInt(values[5]); if (unlikely((__pyx_v_kernel_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 201; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_degree = __Pyx_PyInt_AsInt(values[6]); if (unlikely((__pyx_v_degree == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 201; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_gamma = __pyx_PyFloat_AsDouble(values[7]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 202; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_coef0 = __pyx_PyFloat_AsDouble(values[8]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 202; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_eps = __pyx_PyFloat_AsDouble(values[9]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 202; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __pyx_v_nr_weight = __Pyx_PyInt_AsInt(values[11]); if (unlikely((__pyx_v_nr_weight == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 203; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_v_weight_label = ((PyArrayObject *)values[12]);
__pyx_v_weight = ((PyArrayObject *)values[13]);
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- __pyx_v_cache_size = __pyx_PyFloat_AsDouble(values[15]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 200; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __pyx_v_cache_size = __pyx_PyFloat_AsDouble(values[15]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 206; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __pyx_v_probability = __Pyx_PyInt_AsInt(values[18]); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 207; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __pyx_v_kernel_type = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 5)); if (unlikely((__pyx_v_kernel_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 201; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_degree = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 6)); if (unlikely((__pyx_v_degree == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 201; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":238
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":244
* coef0, nu, cache_size, C, eps, p, shrinking,
* probability, nr_weight, weight_label.data,
* weight.data) # <<<<<<<<<<<<<<
- * model = set_model(param, nr_class, SV.data, SV.shape, sv_coef.strides,
- * sv_coef.data, rho.data, nSV.data, label.data,
+ *
+ * model = set_model(param, nSV.shape[0], SV.data, SV.shape,
*/
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- * probA.data, probB.data) # <<<<<<<<<<<<<<
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":248
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":243
- * probA.data, probB.data)
+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":250
+ * nSV.data, label.data, probA.data, probB.data)
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- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":248
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Py_ssize_t __pyx_bshape_0_sv_coef = 0;
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}
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+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 3); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 4:
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+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 5:
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+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 5); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 6:
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if (likely(values[6])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 6); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 6); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 7:
- values[7] = PyDict_GetItem(__pyx_kwds, __pyx_kp_gamma);
+ values[7] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__gamma);
if (likely(values[7])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 7); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 7); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 8:
- values[8] = PyDict_GetItem(__pyx_kwds, __pyx_kp_coef0);
+ values[8] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__coef0);
if (likely(values[8])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 8); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 8); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 9:
- values[9] = PyDict_GetItem(__pyx_kwds, __pyx_kp_eps);
+ values[9] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__eps);
if (likely(values[9])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 9); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 9); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 10:
- values[10] = PyDict_GetItem(__pyx_kwds, __pyx_kp_C);
+ values[10] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__C);
if (likely(values[10])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 10); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 10); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 11:
- values[11] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nr_weight);
+ values[11] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nr_weight);
if (likely(values[11])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 11); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 11); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 12:
- values[12] = PyDict_GetItem(__pyx_kwds, __pyx_kp_weight_label);
+ values[12] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__weight_label);
if (likely(values[12])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 12); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 12); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 13:
- values[13] = PyDict_GetItem(__pyx_kwds, __pyx_kp_weight);
+ values[13] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__weight);
if (likely(values[13])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 13); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 13); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 14:
- values[14] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nu);
+ values[14] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nu);
if (likely(values[14])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 14); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 14); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 15:
- values[15] = PyDict_GetItem(__pyx_kwds, __pyx_kp_cache_size);
+ values[15] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__cache_size);
if (likely(values[15])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 15); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 15); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 16:
- values[16] = PyDict_GetItem(__pyx_kwds, __pyx_kp_p);
+ values[16] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__p);
if (likely(values[16])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 16); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 16); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 17:
- values[17] = PyDict_GetItem(__pyx_kwds, __pyx_kp_shrinking);
+ values[17] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__shrinking);
if (likely(values[17])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 17); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 17); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 18:
- values[18] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probability);
+ values[18] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probability);
if (likely(values[18])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 18); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 18); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 19:
- values[19] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nr_class);
+ values[19] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nSV);
if (likely(values[19])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 19); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 19); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 20:
- values[20] = PyDict_GetItem(__pyx_kwds, __pyx_kp_nSV);
+ values[20] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__label);
if (likely(values[20])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 20); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 20); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 21:
- values[21] = PyDict_GetItem(__pyx_kwds, __pyx_kp_label);
+ values[21] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probA);
if (likely(values[21])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 21); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 21); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 22:
- values[22] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probA);
+ values[22] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__probB);
if (likely(values[22])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 22); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- }
- case 23:
- values[23] = PyDict_GetItem(__pyx_kwds, __pyx_kp_probB);
- if (likely(values[23])) kw_args--;
- else {
- __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 24, 24, 23); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("predict_prob_from_model_wrap", 1, 23, 23, 22); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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}
if (unlikely(kw_args > 0)) {
- if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "predict_prob_from_model_wrap") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "predict_prob_from_model_wrap") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
__pyx_v_T = ((PyArrayObject *)values[0]);
__pyx_v_SV = ((PyArrayObject *)values[1]);
__pyx_v_sv_coef = ((PyArrayObject *)values[2]);
- __pyx_v_rho = ((PyArrayObject *)values[3]);
- __pyx_v_svm_type = __Pyx_PyInt_AsInt(values[4]); if (unlikely((__pyx_v_svm_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_kernel_type = __Pyx_PyInt_AsInt(values[5]); if (unlikely((__pyx_v_kernel_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_degree = __Pyx_PyInt_AsInt(values[6]); if (unlikely((__pyx_v_degree == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_gamma = __pyx_PyFloat_AsDouble(values[7]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 258; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_coef0 = __pyx_PyFloat_AsDouble(values[8]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 258; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_eps = __pyx_PyFloat_AsDouble(values[9]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 258; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_C = __pyx_PyFloat_AsDouble(values[10]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 259; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_nr_weight = __Pyx_PyInt_AsInt(values[11]); if (unlikely((__pyx_v_nr_weight == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 259; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_intercept = ((PyArrayObject *)values[3]);
+ __pyx_v_svm_type = __Pyx_PyInt_AsInt(values[4]); if (unlikely((__pyx_v_svm_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 264; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_kernel_type = __Pyx_PyInt_AsInt(values[5]); if (unlikely((__pyx_v_kernel_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 264; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_degree = __Pyx_PyInt_AsInt(values[6]); if (unlikely((__pyx_v_degree == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 264; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_gamma = __pyx_PyFloat_AsDouble(values[7]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 265; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_coef0 = __pyx_PyFloat_AsDouble(values[8]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 265; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_eps = __pyx_PyFloat_AsDouble(values[9]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 265; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_C = __pyx_PyFloat_AsDouble(values[10]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 266; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_nr_weight = __Pyx_PyInt_AsInt(values[11]); if (unlikely((__pyx_v_nr_weight == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 266; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_v_weight_label = ((PyArrayObject *)values[12]);
__pyx_v_weight = ((PyArrayObject *)values[13]);
- __pyx_v_nu = __pyx_PyFloat_AsDouble(values[14]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 262; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_cache_size = __pyx_PyFloat_AsDouble(values[15]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 262; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_p = __pyx_PyFloat_AsDouble(values[16]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 262; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_shrinking = __Pyx_PyInt_AsInt(values[17]); if (unlikely((__pyx_v_shrinking == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 262; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_probability = __Pyx_PyInt_AsInt(values[18]); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 263; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_nr_class = __Pyx_PyInt_AsInt(values[19]); if (unlikely((__pyx_v_nr_class == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 263; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_nSV = ((PyArrayObject *)values[20]);
- __pyx_v_label = ((PyArrayObject *)values[21]);
- __pyx_v_probA = ((PyArrayObject *)values[22]);
- __pyx_v_probB = ((PyArrayObject *)values[23]);
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+ __pyx_v_nu = __pyx_PyFloat_AsDouble(values[14]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 269; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_cache_size = __pyx_PyFloat_AsDouble(values[15]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 269; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_p = __pyx_PyFloat_AsDouble(values[16]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 269; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_shrinking = __Pyx_PyInt_AsInt(values[17]); if (unlikely((__pyx_v_shrinking == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 269; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_probability = __Pyx_PyInt_AsInt(values[18]); if (unlikely((__pyx_v_probability == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 270; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __pyx_v_nSV = ((PyArrayObject *)values[19]);
+ __pyx_v_label = ((PyArrayObject *)values[20]);
+ __pyx_v_probA = ((PyArrayObject *)values[21]);
+ __pyx_v_probB = ((PyArrayObject *)values[22]);
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goto __pyx_L5_argtuple_error;
} else {
__pyx_v_T = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 0));
__pyx_v_SV = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 1));
__pyx_v_sv_coef = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 2));
- __pyx_v_rho = ((PyArrayObject *)PyTuple_GET_ITEM(__pyx_args, 3));
- __pyx_v_svm_type = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 4)); if (unlikely((__pyx_v_svm_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_kernel_type = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 5)); if (unlikely((__pyx_v_kernel_type == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_degree = __Pyx_PyInt_AsInt(PyTuple_GET_ITEM(__pyx_args, 6)); if (unlikely((__pyx_v_degree == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_gamma = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 7)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 258; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_coef0 = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 8)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 258; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_eps = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 9)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 258; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
- __pyx_v_C = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 10)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 259; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -3380,7 +3368,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(((PyArrayObject *)__pyx_v_self)));
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":101
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":223
* else:
* info.strides = <Py_ssize_t*>PyArray_STRIDES(self)
* info.shape = <Py_ssize_t*>PyArray_DIMS(self) # <<<<<<<<<<<<<<
@@ -3391,7 +3379,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
}
__pyx_L8:;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":102
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":224
* info.strides = <Py_ssize_t*>PyArray_STRIDES(self)
* info.shape = <Py_ssize_t*>PyArray_DIMS(self)
* info.suboffsets = NULL # <<<<<<<<<<<<<<
@@ -3400,7 +3388,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_info->suboffsets = NULL;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":103
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":225
* info.shape = <Py_ssize_t*>PyArray_DIMS(self)
* info.suboffsets = NULL
* info.itemsize = PyArray_ITEMSIZE(self) # <<<<<<<<<<<<<<
@@ -3409,7 +3397,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_info->itemsize = PyArray_ITEMSIZE(((PyArrayObject *)__pyx_v_self));
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":104
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":226
* info.suboffsets = NULL
* info.itemsize = PyArray_ITEMSIZE(self)
* info.readonly = not PyArray_ISWRITEABLE(self) # <<<<<<<<<<<<<<
@@ -3418,7 +3406,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_info->readonly = (!PyArray_ISWRITEABLE(((PyArrayObject *)__pyx_v_self)));
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":107
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":229
*
* cdef int t
* cdef char* f = NULL # <<<<<<<<<<<<<<
@@ -3427,7 +3415,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_f = NULL;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":108
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":230
* cdef int t
* cdef char* f = NULL
* cdef dtype descr = self.descr # <<<<<<<<<<<<<<
@@ -3437,7 +3425,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
__Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_v_self)->descr));
__pyx_v_descr = ((PyArrayObject *)__pyx_v_self)->descr;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":112
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":234
* cdef int offset
*
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@@ -3446,21 +3434,23 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_hasfields = PyDataType_HASFIELDS(__pyx_v_descr);
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":114
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":236
* cdef bint hasfields = PyDataType_HASFIELDS(descr)
*
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* # do not call releasebuffer
* info.obj = None
*/
- if ((!__pyx_v_hasfields)) {
- __pyx_t_1 = (!__pyx_v_copy_shape);
+ __pyx_t_2 = (!__pyx_v_hasfields);
+ if (__pyx_t_2) {
+ __pyx_t_3 = (!__pyx_v_copy_shape);
+ __pyx_t_1 = __pyx_t_3;
} else {
- __pyx_t_1 = (!__pyx_v_hasfields);
+ __pyx_t_1 = __pyx_t_2;
}
if (__pyx_t_1) {
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":116
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":238
* if not hasfields and not copy_shape:
* # do not call releasebuffer
* info.obj = None # <<<<<<<<<<<<<<
@@ -3476,7 +3466,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
}
/*else*/ {
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":119
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":241
* else:
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@@ -3491,7 +3481,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
}
__pyx_L11:;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":121
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":243
* info.obj = self
*
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@@ -3501,7 +3491,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
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if (__pyx_t_1) {
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":122
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":244
*
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@@ -3510,286 +3500,322 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buf
*/
__pyx_v_t = __pyx_v_descr->type_num;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":123
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":245
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*/
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+ if (__pyx_t_1) {
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- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":124
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":246
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* if ((descr.byteorder == '>' and little_endian) or
* (descr.byteorder == '<' and not little_endian)): # <<<<<<<<<<<<<<
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*/
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- if (__pyx_t_6) {
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- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":125
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* if ((descr.byteorder == '>' and little_endian) or
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goto __pyx_L13;
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__pyx_L13:;
- /* "/usr/lib/pymodules/python2.6/Cython/Includes/numpy.pxd":126
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* (descr.byteorder == '<' and not little_endian)):
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*/
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- break;
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+ __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
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@@ -4770,32 +5147,37 @@ PyMODINIT_FUNC PyInit_libsvm(void); /*proto*/
PyMODINIT_FUNC PyInit_libsvm(void)
#endif
{
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- void* __pyx_refchk = NULL;
- __Pyx_Refnanny = __Pyx_ImportRefcountAPI("refnanny");
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+ __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny");
+ if (!__Pyx_RefNanny)
+ Py_FatalError("failed to import 'refnanny' module");
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+ __pyx_refnanny = __Pyx_RefNanny->SetupContext("PyMODINIT_FUNC PyInit_libsvm(void)", __LINE__, __FILE__);
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+ #if PY_MAJOR_VERSION < 3
+ __pyx_empty_bytes = PyString_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ #else
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+ #endif
/*--- Library function declarations ---*/
- __pyx_init_filenames();
/*--- Threads initialization code ---*/
#if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS
#ifdef WITH_THREAD /* Python build with threading support? */
PyEval_InitThreads();
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#endif
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- __pyx_m = Py_InitModule4(__Pyx_NAMESTR("libsvm"), __pyx_methods, __pyx_mdoc, 0, PYTHON_API_VERSION);
+ __pyx_m = Py_InitModule4(__Pyx_NAMESTR("libsvm"), __pyx_methods, __Pyx_DOCSTR(__pyx_k_9), 0, PYTHON_API_VERSION);
#else
__pyx_m = PyModule_Create(&__pyx_moduledef);
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+ /*--- Initialize various global constants etc. ---*/
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+ __pyx_ptype_5numpy_broadcast = __Pyx_ImportType("numpy", "broadcast", sizeof(PyArrayMultiIterObject)); if (unlikely(!__pyx_ptype_5numpy_broadcast)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 162; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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/*--- Function import code ---*/
/*--- Execution code ---*/
- /* "/home/fabian/dev/scikit-learn-svn/scikits/learn/src/libsvm.pyx":41
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- __Pyx_DECREF(__pyx_1); __pyx_1 = 0;
+ __pyx_t_1 = __Pyx_Import(((PyObject *)__pyx_n_s__numpy), 0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 41; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_1);
+ if (PyObject_SetAttr(__pyx_m, __pyx_n_s__np, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 41; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
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+ /* "/Users/fabian/dev/scikit-learn/scikits/learn/src/libsvm.pyx":1
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+ __Pyx_GOTREF(((PyObject *)__pyx_t_1));
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+ __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+ __pyx_t_3 = PyObject_GetAttr(__pyx_m, __pyx_n_s_12); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_3);
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+ __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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+ if (PyObject_SetAttr(__pyx_m, __pyx_n_s____test__, ((PyObject *)__pyx_t_1)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- /* "/usr/lib/pymodules/python2.6/Cython/Includes/stdlib.pxd":2
+ /* "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/Cython/Includes/stdlib.pxd":2
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goto __pyx_L0;
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- __Pyx_XDECREF(__pyx_1);
- __Pyx_AddTraceback("libsvm");
- Py_DECREF(__pyx_m); __pyx_m = 0;
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+ __Pyx_XDECREF(__pyx_t_2);
+ __Pyx_XDECREF(__pyx_t_3);
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+ __Pyx_AddTraceback("init libsvm");
+ Py_DECREF(__pyx_m); __pyx_m = 0;
+ } else if (!PyErr_Occurred()) {
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return;
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+}
+
+static INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
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PyErr_Format(PyExc_SystemError, "Missing type object");
return 0;
}
- if (obj == Py_None || PyObject_TypeCheck(obj, type))
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return 1;
- PyErr_Format(PyExc_TypeError, "Cannot convert %s to %s",
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+ PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
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return 0;
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tmp_tb = tstate->curexc_traceback;
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+}
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+ PyErr_SetString(PyExc_ValueError, "too many values to unpack");
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+ __Pyx_RaiseTooManyValuesError();
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+}
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+static void __Pyx_UnpackTupleError(PyObject *t, Py_ssize_t index) {
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+ __Pyx_RaiseNoneNotIterableError();
+ } else if (PyTuple_GET_SIZE(t) < index) {
+ __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(t));
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+ __Pyx_RaiseTooManyValuesError();
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+}
static int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
const char *name, int exact)
@@ -5578,6 +6041,7 @@ static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) {
return result;
}
+#if PY_MAJOR_VERSION < 3
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
Py_XINCREF(type);
Py_XINCREF(value);
@@ -5633,6 +6097,7 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
}
#endif
}
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__Pyx_ErrRestore(type, value, tb);
return;
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+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
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+ } else if (tb && !PyTraceBack_Check(tb)) {
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+ goto bad;
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+ PyErr_SetObject(type, value);
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+
+bad:
+ return;
+}
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+ } else if (sizeof(npy_intp) == sizeof(long)) {
+ if (is_unsigned)
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+ else
+ return PyInt_FromLong((long)val);
+ } else { /* (sizeof(npy_intp) > sizeof(long)) */
+ if (is_unsigned)
+ return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG)val);
+ else
+ return PyLong_FromLongLong((PY_LONG_LONG)val);
+ }
}
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+ return ::std::complex< float >(x, y);
+ }
+ #else
+ static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+ return x + y*(__pyx_t_float_complex)_Complex_I;
+ }
+ #endif
+#else
+ static INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+ __pyx_t_float_complex z;
+ z.real = x;
+ z.imag = y;
+ return z;
+ }
+#endif
+
+#if CYTHON_CCOMPLEX
+#else
+ static INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+ return (a.real == b.real) && (a.imag == b.imag);
+ }
+ static INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+ __pyx_t_float_complex z;
+ z.real = a.real + b.real;
+ z.imag = a.imag + b.imag;
+ return z;
+ }
+ static INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+ __pyx_t_float_complex z;
+ z.real = a.real - b.real;
+ z.imag = a.imag - b.imag;
+ return z;
+ }
+ static INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+ __pyx_t_float_complex z;
+ z.real = a.real * b.real - a.imag * b.imag;
+ z.imag = a.real * b.imag + a.imag * b.real;
+ return z;
+ }
+ static INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+ __pyx_t_float_complex z;
+ float denom = b.real * b.real + b.imag * b.imag;
+ z.real = (a.real * b.real + a.imag * b.imag) / denom;
+ z.imag = (a.imag * b.real - a.real * b.imag) / denom;
+ return z;
+ }
+ static INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) {
+ __pyx_t_float_complex z;
+ z.real = -a.real;
+ z.imag = -a.imag;
+ return z;
+ }
+ static INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) {
+ return (a.real == 0) && (a.imag == 0);
+ }
+ static INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) {
+ __pyx_t_float_complex z;
+ z.real = a.real;
+ z.imag = -a.imag;
+ return z;
+ }
+/*
+ static INLINE float __Pyx_c_absf(__pyx_t_float_complex z) {
+#if HAVE_HYPOT
+ return hypotf(z.real, z.imag);
+#else
+ return sqrtf(z.real*z.real + z.imag*z.imag);
+#endif
+ }
+*/
+#endif
+
+#if CYTHON_CCOMPLEX
+ #ifdef __cplusplus
+ static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+ return ::std::complex< double >(x, y);
+ }
+ #else
+ static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+ return x + y*(__pyx_t_double_complex)_Complex_I;
+ }
+ #endif
+#else
+ static INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+ __pyx_t_double_complex z;
+ z.real = x;
+ z.imag = y;
+ return z;
+ }
+#endif
+
+#if CYTHON_CCOMPLEX
+#else
+ static INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+ return (a.real == b.real) && (a.imag == b.imag);
+ }
+ static INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+ __pyx_t_double_complex z;
+ z.real = a.real + b.real;
+ z.imag = a.imag + b.imag;
+ return z;
+ }
+ static INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+ __pyx_t_double_complex z;
+ z.real = a.real - b.real;
+ z.imag = a.imag - b.imag;
+ return z;
+ }
+ static INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+ __pyx_t_double_complex z;
+ z.real = a.real * b.real - a.imag * b.imag;
+ z.imag = a.real * b.imag + a.imag * b.real;
+ return z;
+ }
+ static INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+ __pyx_t_double_complex z;
+ double denom = b.real * b.real + b.imag * b.imag;
+ z.real = (a.real * b.real + a.imag * b.imag) / denom;
+ z.imag = (a.imag * b.real - a.real * b.imag) / denom;
+ return z;
+ }
+ static INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) {
+ __pyx_t_double_complex z;
+ z.real = -a.real;
+ z.imag = -a.imag;
+ return z;
+ }
+ static INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) {
+ return (a.real == 0) && (a.imag == 0);
+ }
+ static INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) {
+ __pyx_t_double_complex z;
+ z.real = a.real;
+ z.imag = -a.imag;
+ return z;
+ }
+/*
+ static INLINE double __Pyx_c_abs(__pyx_t_double_complex z) {
+#if HAVE_HYPOT
+ return hypot(z.real, z.imag);
+#else
+ return sqrt(z.real*z.real + z.imag*z.imag);
+#endif
+ }
+*/
+#endif
+
static INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) {
+ const unsigned char neg_one = (unsigned char)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(unsigned char) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(unsigned char)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (unsigned char)-1;
- if (unlikely(val < 0)) {
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to unsigned char");
- return (unsigned char)-1;
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to unsigned char" :
+ "value too large to convert to unsigned char");
}
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to unsigned char");
return (unsigned char)-1;
}
return (unsigned char)val;
@@ -5668,18 +6339,17 @@ static INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) {
}
static INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) {
+ const unsigned short neg_one = (unsigned short)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(unsigned short) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(unsigned short)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (unsigned short)-1;
- if (unlikely(val < 0)) {
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to unsigned short");
- return (unsigned short)-1;
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to unsigned short" :
+ "value too large to convert to unsigned short");
}
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to unsigned short");
return (unsigned short)-1;
}
return (unsigned short)val;
@@ -5688,18 +6358,17 @@ static INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) {
}
static INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) {
+ const unsigned int neg_one = (unsigned int)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(unsigned int) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(unsigned int)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (unsigned int)-1;
- if (unlikely(val < 0)) {
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to unsigned int");
- return (unsigned int)-1;
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to unsigned int" :
+ "value too large to convert to unsigned int");
}
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to unsigned int");
return (unsigned int)-1;
}
return (unsigned int)val;
@@ -5708,13 +6377,17 @@ static INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) {
}
static INLINE char __Pyx_PyInt_AsChar(PyObject* x) {
+ const char neg_one = (char)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(char) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(char)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (char)-1;
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to char");
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
+ PyErr_SetString(PyExc_OverflowError,
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to char" :
+ "value too large to convert to char");
+ }
return (char)-1;
}
return (char)val;
@@ -5723,13 +6396,17 @@ static INLINE char __Pyx_PyInt_AsChar(PyObject* x) {
}
static INLINE short __Pyx_PyInt_AsShort(PyObject* x) {
+ const short neg_one = (short)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(short) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(short)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (short)-1;
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to short");
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
+ PyErr_SetString(PyExc_OverflowError,
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to short" :
+ "value too large to convert to short");
+ }
return (short)-1;
}
return (short)val;
@@ -5738,13 +6415,17 @@ static INLINE short __Pyx_PyInt_AsShort(PyObject* x) {
}
static INLINE int __Pyx_PyInt_AsInt(PyObject* x) {
+ const int neg_one = (int)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(int) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(int)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (int)-1;
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to int");
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
+ PyErr_SetString(PyExc_OverflowError,
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to int" :
+ "value too large to convert to int");
+ }
return (int)-1;
}
return (int)val;
@@ -5753,13 +6434,17 @@ static INLINE int __Pyx_PyInt_AsInt(PyObject* x) {
}
static INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) {
+ const signed char neg_one = (signed char)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(signed char) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(signed char)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (signed char)-1;
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to signed char");
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
+ PyErr_SetString(PyExc_OverflowError,
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to signed char" :
+ "value too large to convert to signed char");
+ }
return (signed char)-1;
}
return (signed char)val;
@@ -5768,13 +6453,17 @@ static INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) {
}
static INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) {
+ const signed short neg_one = (signed short)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(signed short) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(signed short)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (signed short)-1;
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to signed short");
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
+ PyErr_SetString(PyExc_OverflowError,
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to signed short" :
+ "value too large to convert to signed short");
+ }
return (signed short)-1;
}
return (signed short)val;
@@ -5783,13 +6472,17 @@ static INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) {
}
static INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) {
+ const signed int neg_one = (signed int)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
if (sizeof(signed int) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(signed int)val)) {
- if (unlikely(val == -1 && PyErr_Occurred()))
- return (signed int)-1;
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to signed int");
+ if (!unlikely(val == -1 && PyErr_Occurred())) {
+ PyErr_SetString(PyExc_OverflowError,
+ (is_unsigned && unlikely(val < 0)) ?
+ "can't convert negative value to signed int" :
+ "value too large to convert to signed int");
+ }
return (signed int)-1;
}
return (signed int)val;
@@ -5798,10 +6491,12 @@ static INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) {
}
static INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) {
+ const unsigned long neg_one = (unsigned long)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
#if PY_VERSION_HEX < 0x03000000
- if (likely(PyInt_CheckExact(x) || PyInt_Check(x))) {
+ if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
- if (unlikely(val < 0)) {
+ if (is_unsigned && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to unsigned long");
return (unsigned long)-1;
@@ -5809,13 +6504,17 @@ static INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) {
return (unsigned long)val;
} else
#endif
- if (likely(PyLong_CheckExact(x) || PyLong_Check(x))) {
- if (unlikely(Py_SIZE(x) < 0)) {
- PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to unsigned long");
- return (unsigned long)-1;
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+ if (unlikely(Py_SIZE(x) < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to unsigned long");
+ return (unsigned long)-1;
+ }
+ return PyLong_AsUnsignedLong(x);
+ } else {
+ return PyLong_AsLong(x);
}
- return PyLong_AsUnsignedLong(x);
} else {
unsigned long val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
@@ -5827,10 +6526,12 @@ static INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) {
}
static INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) {
+ const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
#if PY_VERSION_HEX < 0x03000000
- if (likely(PyInt_CheckExact(x) || PyInt_Check(x))) {
+ if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
- if (unlikely(val < 0)) {
+ if (is_unsigned && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to unsigned PY_LONG_LONG");
return (unsigned PY_LONG_LONG)-1;
@@ -5838,13 +6539,17 @@ static INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x)
return (unsigned PY_LONG_LONG)val;
} else
#endif
- if (likely(PyLong_CheckExact(x) || PyLong_Check(x))) {
- if (unlikely(Py_SIZE(x) < 0)) {
- PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to unsigned PY_LONG_LONG");
- return (unsigned PY_LONG_LONG)-1;
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+ if (unlikely(Py_SIZE(x) < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to unsigned PY_LONG_LONG");
+ return (unsigned PY_LONG_LONG)-1;
+ }
+ return PyLong_AsUnsignedLongLong(x);
+ } else {
+ return PyLong_AsLongLong(x);
}
- return PyLong_AsUnsignedLongLong(x);
} else {
unsigned PY_LONG_LONG val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
@@ -5856,14 +6561,30 @@ static INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x)
}
static INLINE long __Pyx_PyInt_AsLong(PyObject* x) {
+ const long neg_one = (long)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
#if PY_VERSION_HEX < 0x03000000
- if (likely(PyInt_CheckExact(x) || PyInt_Check(x))) {
+ if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
+ if (is_unsigned && unlikely(val < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to long");
+ return (long)-1;
+ }
return (long)val;
} else
#endif
- if (likely(PyLong_CheckExact(x) || PyLong_Check(x))) {
- return PyLong_AsLong(x);
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+ if (unlikely(Py_SIZE(x) < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to long");
+ return (long)-1;
+ }
+ return PyLong_AsUnsignedLong(x);
+ } else {
+ return PyLong_AsLong(x);
+ }
} else {
long val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
@@ -5875,14 +6596,30 @@ static INLINE long __Pyx_PyInt_AsLong(PyObject* x) {
}
static INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) {
+ const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
#if PY_VERSION_HEX < 0x03000000
- if (likely(PyInt_CheckExact(x) || PyInt_Check(x))) {
+ if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
+ if (is_unsigned && unlikely(val < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to PY_LONG_LONG");
+ return (PY_LONG_LONG)-1;
+ }
return (PY_LONG_LONG)val;
} else
#endif
- if (likely(PyLong_CheckExact(x) || PyLong_Check(x))) {
- return PyLong_AsLongLong(x);
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+ if (unlikely(Py_SIZE(x) < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to PY_LONG_LONG");
+ return (PY_LONG_LONG)-1;
+ }
+ return PyLong_AsUnsignedLongLong(x);
+ } else {
+ return PyLong_AsLongLong(x);
+ }
} else {
PY_LONG_LONG val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
@@ -5894,14 +6631,30 @@ static INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) {
}
static INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) {
+ const signed long neg_one = (signed long)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
#if PY_VERSION_HEX < 0x03000000
- if (likely(PyInt_CheckExact(x) || PyInt_Check(x))) {
+ if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
+ if (is_unsigned && unlikely(val < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to signed long");
+ return (signed long)-1;
+ }
return (signed long)val;
} else
#endif
- if (likely(PyLong_CheckExact(x) || PyLong_Check(x))) {
- return PyLong_AsLong(x);
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+ if (unlikely(Py_SIZE(x) < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to signed long");
+ return (signed long)-1;
+ }
+ return PyLong_AsUnsignedLong(x);
+ } else {
+ return PyLong_AsLong(x);
+ }
} else {
signed long val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
@@ -5913,14 +6666,30 @@ static INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) {
}
static INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) {
+ const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0;
+ const int is_unsigned = neg_one > const_zero;
#if PY_VERSION_HEX < 0x03000000
- if (likely(PyInt_CheckExact(x) || PyInt_Check(x))) {
+ if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
+ if (is_unsigned && unlikely(val < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to signed PY_LONG_LONG");
+ return (signed PY_LONG_LONG)-1;
+ }
return (signed PY_LONG_LONG)val;
} else
#endif
- if (likely(PyLong_CheckExact(x) || PyLong_Check(x))) {
- return PyLong_AsLongLong(x);
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+ if (unlikely(Py_SIZE(x) < 0)) {
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to signed PY_LONG_LONG");
+ return (signed PY_LONG_LONG)-1;
+ }
+ return PyLong_AsUnsignedLongLong(x);
+ } else {
+ return PyLong_AsLongLong(x);
+ }
} else {
signed PY_LONG_LONG val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
@@ -6007,7 +6776,6 @@ static void __Pyx_AddTraceback(const char *funcname) {
PyObject *py_srcfile = 0;
PyObject *py_funcname = 0;
PyObject *py_globals = 0;
- PyObject *empty_string = 0;
PyCodeObject *py_code = 0;
PyFrameObject *py_frame = 0;
@@ -6034,12 +6802,6 @@ static void __Pyx_AddTraceback(const char *funcname) {
if (!py_funcname) goto bad;
py_globals = PyModule_GetDict(__pyx_m);
if (!py_globals) goto bad;
- #if PY_MAJOR_VERSION < 3
- empty_string = PyString_FromStringAndSize("", 0);
- #else
- empty_string = PyBytes_FromStringAndSize("", 0);
- #endif
- if (!empty_string) goto bad;
py_code = PyCode_New(
0, /*int argcount,*/
#if PY_MAJOR_VERSION >= 3
@@ -6048,7 +6810,7 @@ static void __Pyx_AddTraceback(const char *funcname) {
0, /*int nlocals,*/
0, /*int stacksize,*/
0, /*int flags,*/
- empty_string, /*PyObject *code,*/
+ __pyx_empty_bytes, /*PyObject *code,*/
__pyx_empty_tuple, /*PyObject *consts,*/
__pyx_empty_tuple, /*PyObject *names,*/
__pyx_empty_tuple, /*PyObject *varnames,*/
@@ -6057,7 +6819,7 @@ static void __Pyx_AddTraceback(const char *funcname) {
py_srcfile, /*PyObject *filename,*/
py_funcname, /*PyObject *name,*/
__pyx_lineno, /*int firstlineno,*/
- empty_string /*PyObject *lnotab*/
+ __pyx_empty_bytes /*PyObject *lnotab*/
);
if (!py_code) goto bad;
py_frame = PyFrame_New(
@@ -6072,7 +6834,6 @@ static void __Pyx_AddTraceback(const char *funcname) {
bad:
Py_XDECREF(py_srcfile);
Py_XDECREF(py_funcname);
- Py_XDECREF(empty_string);
Py_XDECREF(py_code);
Py_XDECREF(py_frame);
}
@@ -6080,7 +6841,7 @@ bad:
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
while (t->p) {
#if PY_MAJOR_VERSION < 3
- if (t->is_unicode && (!t->is_identifier)) {
+ if (t->is_unicode) {
*t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
} else if (t->intern) {
*t->p = PyString_InternFromString(t->s);
@@ -6088,10 +6849,14 @@ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
*t->p = PyString_FromStringAndSize(t->s, t->n - 1);
}
#else /* Python 3+ has unicode identifiers */
- if (t->is_identifier || (t->is_unicode && t->intern)) {
- *t->p = PyUnicode_InternFromString(t->s);
- } else if (t->is_unicode) {
- *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
+ if (t->is_unicode | t->is_str) {
+ if (t->intern) {
+ *t->p = PyUnicode_InternFromString(t->s);
+ } else if (t->encoding) {
+ *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
+ } else {
+ *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
+ }
} else {
*t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
}
@@ -6193,3 +6958,4 @@ static INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) {
}
+#endif /* Py_PYTHON_H */
diff --git a/scikits/learn/src/libsvm.pyx b/scikits/learn/src/libsvm.pyx
index ed081dd989..0c16dc27e0 100644
--- a/scikits/learn/src/libsvm.pyx
+++ b/scikits/learn/src/libsvm.pyx
@@ -62,11 +62,11 @@ cdef extern from "libsvm_helper.c":
svm_problem *set_problem(char *, char *, np.npy_intp *)
svm_model *set_model(svm_parameter *, int, char *, np.npy_intp *, np.npy_intp *,
char *, char *, char *, char *, char *, char *)
- void copy_sv_coef (char *, svm_model *, np.npy_intp *)
- void copy_rho (char *, svm_model *, np.npy_intp *)
- void copy_SV (char *, svm_model *, np.npy_intp *)
+ void copy_sv_coef (char *, svm_model *, np.npy_intp *)
+ void copy_intercept (char *, svm_model *, np.npy_intp *)
+ void copy_SV (char *, svm_model *, np.npy_intp *)
int copy_predict (char *, svm_model *, np.npy_intp *, char *)
- int copy_prob_predict (char *, svm_model *, np.npy_intp *, char *)
+ int copy_predict_proba (char *, svm_model *, np.npy_intp *, char *)
void copy_nSV (char *, svm_model *)
void copy_label (char *, svm_model *)
void copy_probA(char *, svm_model *, np.npy_intp *)
@@ -86,12 +86,16 @@ def train_wrap ( np.ndarray[np.float64_t, ndim=2, mode='c'] X,
np.ndarray[np.float64_t, ndim=1, mode='c'] Y, int
svm_type, int kernel_type, int degree, double gamma,
double coef0, double eps, double C, int nr_weight,
- np.ndarray[np.int32_t, ndim=1] weight_label,
- np.ndarray[np.float64_t, ndim=1] weight, double nu,
- double cache_size, double p, int shrinking, int
- probability):
+ np.ndarray[np.float64_t, ndim=2, mode='c'] SV,
+ np.ndarray[np.float64_t, ndim=2, mode='c'] sv_coef,
+ np.ndarray[np.float64_t, ndim=1, mode='c'] intercept,
+ np.ndarray[np.int32_t, ndim=1, mode='c'] weight_label,
+ np.ndarray[np.float64_t, ndim=1, mode='c'] weight,
+ np.ndarray[np.int32_t, ndim=1, mode='c'] nclass_SV,
+ double nu, double cache_size, double p, int
+ shrinking, int probability):
"""
- Wrapper for svm_train in libsvm
+ Wrap svm_train from libsvm
Parameters
----------
@@ -100,7 +104,9 @@ def train_wrap ( np.ndarray[np.float64_t, ndim=2, mode='c'] X,
Y: array, dtype=float, size=[N]
target vector
- Optional Parameters
+ ...
+
+ Notes
-------------------
See scikits.learn.svm.predict for a complete list of parameters.
@@ -108,7 +114,7 @@ def train_wrap ( np.ndarray[np.float64_t, ndim=2, mode='c'] X,
------
sv_coef: array of coeficients for support vector in decision
function (aka alphas)
- rho : array
+ intercept : array
constants in decision functions
SV : array-like
support vectors
@@ -141,29 +147,29 @@ def train_wrap ( np.ndarray[np.float64_t, ndim=2, mode='c'] X,
# call svm_train, this does the real work
model = svm_train(problem, param)
- cdef int nSV = get_l(model)
+ cdef int SV_len = get_l(model)
cdef int nr = get_nr(model)
- # copy model.sv_coef
- cdef np.ndarray[np.float64_t, ndim=2, mode='c'] sv_coef
- sv_coef = np.empty((nr-1, nSV))
+ # copy model.sv_coef
+ # we create a new array instead of resizing, otherwise
+ # it would not erase previous information
+ sv_coef.resize((nr-1, SV_len), refcheck=False)
copy_sv_coef(sv_coef.data, model, sv_coef.strides)
- # copy model.rho
- cdef np.ndarray[np.float64_t, ndim=1, mode='c'] rho
- rho = np.empty(nr*(nr-1)/2)
- copy_rho(rho.data, model, rho.shape)
+ # copy model.rho into the intercept
+ # the intercept is just model.rho but with sign changed
+ intercept.resize(nr*(nr-1)/2, refcheck=False)
+ copy_intercept(intercept.data, model, intercept.shape)
# copy model.SV
- cdef np.ndarray[np.float64_t, ndim=2, mode='c'] SV
- SV = np.zeros((nSV, X.shape[1]))
- copy_SV(SV.data, model, SV.strides)
+ # we erase any previous information in SV
+ SV.resize((0,0), refcheck=False)
+ SV.resize((SV_len, X.shape[1]), refcheck=False)
+ copy_SV(SV.data, model, SV.shape)
# copy model.nSV
- # name is a bit confusing since we used nSV to denote the total number
- # of support vectors
- cdef np.ndarray[np.int32_t, ndim=1, mode='c'] nclass_SV
- nclass_SV = np.empty((nr), dtype=np.int32)
+ # TODO: do only in classification
+ nclass_SV.resize(nr, refcheck=False)
copy_nSV(nclass_SV.data, model)
# copy label
@@ -185,20 +191,20 @@ def train_wrap ( np.ndarray[np.float64_t, ndim=2, mode='c'] X,
free_problem(problem)
free_param(param)
- return sv_coef, rho, SV, nr, nclass_SV, label, probA, probB
+ return label, probA, probB
def predict_from_model_wrap(np.ndarray[np.float64_t, ndim=2, mode='c'] T,
np.ndarray[np.float64_t, ndim=2, mode='c'] SV,
np.ndarray[np.float64_t, ndim=2, mode='c'] sv_coef,
np.ndarray[np.float64_t, ndim=1, mode='c']
- rho, int svm_type, int kernel_type, int
+ intercept, int svm_type, int kernel_type, int
degree, double gamma, double coef0, double
eps, double C, int nr_weight,
np.ndarray[np.int32_t, ndim=1] weight_label,
np.ndarray[np.float64_t, ndim=1] weight,
double nu, double cache_size, double p, int
- shrinking, int probability, int nr_class,
+ shrinking, int probability,
np.ndarray[np.int32_t, ndim=1, mode='c'] nSV,
np.ndarray[np.int32_t, ndim=1, mode='c'] label,
np.ndarray[np.float64_t, ndim=1, mode='c'] probA,
@@ -236,9 +242,10 @@ def predict_from_model_wrap(np.ndarray[np.float64_t, ndim=2, mode='c'] T,
coef0, nu, cache_size, C, eps, p, shrinking,
probability, nr_weight, weight_label.data,
weight.data)
- model = set_model(param, nr_class, SV.data, SV.shape, sv_coef.strides,
- sv_coef.data, rho.data, nSV.data, label.data,
- probA.data, probB.data)
+
+ model = set_model(param, nSV.shape[0], SV.data, SV.shape,
+ sv_coef.strides, sv_coef.data, intercept.data,
+ nSV.data, label.data, probA.data, probB.data)
#TODO: use check_model
dec_values = np.empty(T.shape[0])
if copy_predict(T.data, model, T.shape, dec_values.data) < 0:
@@ -254,13 +261,13 @@ def predict_prob_from_model_wrap(np.ndarray[np.float64_t, ndim=2, mode='c'] T,
np.ndarray[np.float64_t, ndim=2, mode='c'] SV,
np.ndarray[np.float64_t, ndim=2, mode='c'] sv_coef,
np.ndarray[np.float64_t, ndim=1, mode='c']
- rho, int svm_type, int kernel_type, int
+ intercept, int svm_type, int kernel_type, int
degree, double gamma, double coef0, double
eps, double C, int nr_weight,
np.ndarray[np.int32_t, ndim=1] weight_label,
np.ndarray[np.float_t, ndim=1] weight,
double nu, double cache_size, double p, int
- shrinking, int probability, int nr_class,
+ shrinking, int probability,
np.ndarray[np.int32_t, ndim=1, mode='c'] nSV,
np.ndarray[np.int32_t, ndim=1, mode='c'] label,
np.ndarray[np.float64_t, ndim=1, mode='c'] probA,
@@ -298,13 +305,13 @@ def predict_prob_from_model_wrap(np.ndarray[np.float64_t, ndim=2, mode='c'] T,
coef0, nu, cache_size, C, eps, p, shrinking,
probability, nr_weight, weight_label.data,
weight.data)
- model = set_model(param, nr_class, SV.data, SV.shape, sv_coef.strides,
- sv_coef.data, rho.data, nSV.data, label.data,
+ model = set_model(param, nSV.shape[0], SV.data, SV.shape, sv_coef.strides,
+ sv_coef.data, intercept.data, nSV.data, label.data,
probA.data, probB.data)
cdef int nr = get_nr(model)
dec_values = np.empty((T.shape[0], nr), dtype=np.float64)
- if copy_prob_predict(T.data, model, T.shape, dec_values.data) < 0:
+ if copy_predict_proba(T.data, model, T.shape, dec_values.data) < 0:
raise MemoryError("We've run out of of memory")
# free model and param
free_model_SV(model)
diff --git a/scikits/learn/src/libsvm_helper.c b/scikits/learn/src/libsvm_helper.c
index 56bb31ca6e..136d997f07 100644
--- a/scikits/learn/src/libsvm_helper.c
+++ b/scikits/learn/src/libsvm_helper.c
@@ -1,4 +1,5 @@
#include <stdlib.h>
+#include <stdio.h>
#include "svm.h"
#include <numpy/arrayobject.h>
@@ -189,7 +190,9 @@ struct svm_model *set_model(struct svm_parameter *param, int nr_class,
t += sv_coef_strides[0];
}
- memcpy(model->rho, rho, m * sizeof(double));
+ for (i=0; i<m; ++i)
+ (model->rho)[i] = -((double *) rho)[i];
+
/*
* just to avoid segfaults, these features are not wrapped but
* svm_destroy_model will try to free them.
@@ -244,9 +247,17 @@ void copy_sv_coef(char *data, struct svm_model *model, npy_intp *strides)
}
}
-void copy_rho(char *data, struct svm_model *model, npy_intp *dims)
+void copy_intercept(char *data, struct svm_model *model, npy_intp *dims)
{
- memcpy(data, model->rho, dims[0] * sizeof(double));
+ /* intercept = -rho */
+ int i, n = dims[0];
+ double t, *ddata = (double *) data;
+ for (i=0; i<n; ++i) {
+ t = model->rho[i];
+ /* we do this to avoid ugly -0.0 */
+ *ddata = (t != 0) ? -t : 0;
+ ++ddata;
+ }
}
/*
@@ -254,19 +265,18 @@ void copy_rho(char *data, struct svm_model *model, npy_intp *dims)
* structures, so we have to do the conversion on the fly and also
* iterate fast over data.
*/
-void copy_SV(char *data, struct svm_model *model, npy_intp *strides)
+void copy_SV(char *data, struct svm_model *model, npy_intp *dims)
{
- register int i, j;
- char *t = data;
- int k, n = model->l;
- npy_intp step = strides[1];
+ int i, j, k, n = model->l;
+ double *t = (double *) data;
+ int step = dims[1] * sizeof(double);
for (i=0; i<n; ++i) {
- k = model->SV[i][0].index;
- for(j=0; k >= 0; ++j) {
- * ((double *) (t + (k-1)*step)) = model->SV[i][j].value;
- k = model->SV[i][j+1].index;
+ k = model->SV[i][0].index - 1;
+ for(j=0; k >=0 ; ++j) {
+ t[k] = model->SV[i][j].value;
+ k = model->SV[i][j+1].index - 1;
}
- t += strides[0];
+ t += dims[1];
}
}
@@ -326,10 +336,9 @@ int copy_predict(char *predict, struct svm_model *model, npy_intp *predict_dims,
return 0;
}
-int copy_prob_predict(char *predict, struct svm_model *model, npy_intp *predict_dims,
+int copy_predict_proba(char *predict, struct svm_model *model, npy_intp *predict_dims,
char *dec_values)
{
- double *t = (double *) dec_values;
register int i;
int n, m;
n = predict_dims[0];
diff --git a/scikits/learn/svm.py b/scikits/learn/svm.py
index acb19a9c75..8282943708 100644
--- a/scikits/learn/svm.py
+++ b/scikits/learn/svm.py
@@ -1,13 +1,11 @@
import numpy as np
from . import libsvm, liblinear
-_kernel_types = ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']
-_svm_types = ['c_svc', 'nu_svc', 'one_class', 'epsilon_svr', 'nu_svr']
-
class BaseLibsvm(object):
"""
- Base class for classifiers that use support vector machine.
+ Base class for classifiers that use libsvm as library for
+ support vector machine classification and regression.
Should not be used directly, use derived classes instead
@@ -19,17 +17,23 @@ class BaseLibsvm(object):
target vector relative to X
It will be converted to a floating-point array.
"""
- support_ = None
- dual_coef_ = None
- rho_ = None
+ support_ = np.empty((0,0), dtype=np.float64, order='C')
+ dual_coef_ = np.empty((0,0), dtype=np.float64, order='C')
+ intercept_ = np.empty(0, dtype=np.float64, order='C')
+
+ # only used in classification
+ nSV_ = np.empty(0, dtype=np.int32, order='C')
+
+ weight = np.empty(0, dtype=np.float64, order='C')
+ weight_label = np.empty(0, dtype=np.int32, order='C')
- weight = np.empty(0, dtype=np.float64)
- weight_label = np.empty(0, dtype=np.int32)
+ _kernel_types = ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']
+ _svm_types = ['c_svc', 'nu_svc', 'one_class', 'epsilon_svr', 'nu_svr']
def __init__(self, impl, kernel, degree, gamma, coef0, cache_size,
eps, C, nr_weight, nu, p, shrinking, probability):
- self.svm = _svm_types.index(impl)
- self.kernel = _kernel_types.index(kernel)
+ self.solver_type = self._svm_types.index(impl)
+ self.kernel = self._kernel_types.index(kernel)
self.degree = degree
self.gamma = gamma
self.coef0 = coef0
@@ -42,9 +46,9 @@ class BaseLibsvm(object):
self.shrinking = int(shrinking)
self.probability = int(probability)
- def fit(self, X, y):
+ def fit(self, X, Y):
"""
- Fit the model with vectors X, Y.
+ Fit the SVM model according to the given training data and parameters.
Parameters
----------
@@ -52,54 +56,86 @@ class BaseLibsvm(object):
Training vector, where nsamples in the number of samples and
nfeatures is the number of features.
Y : array, shape = [nsamples]
- Target vector relative to X
-
+ Target values (integers in classification, real numbers in
+ regression)
"""
X = np.asanyarray(X, dtype=np.float64, order='C')
- y = np.asanyarray(y, dtype=np.float64, order='C')
+ Y = np.asanyarray(Y, dtype=np.float64, order='C')
# check dimensions
- if X.shape[0] != y.shape[0]: raise ValueError("Incompatible shapes")
+ if X.shape[0] != Y.shape[0]: raise ValueError("Incompatible shapes")
if (self.gamma == 0): self.gamma = 1.0/X.shape[0]
- self.dual_coef_, self.rho_, self.support_, self.nclass_, self.nSV_, \
- self.label_, self.probA_, self.probB_ = libsvm.train_wrap(X, y,
- self.svm, self.kernel, self.degree, self.gamma,
- self.coef0, self.eps, self.C, self.nr_weight,
- self.weight_label, self.weight, self.nu, self.cache_size, self.p,
- self.shrinking, int(self.probability))
+ self.label_, self.probA_, self.probB_ = libsvm.train_wrap(X, Y,
+ self.solver_type, self.kernel, self.degree,
+ self.gamma, self.coef0, self.eps, self.C,
+ self.nr_weight, self.support_, self.dual_coef_,
+ self.intercept_, self.weight_label, self.weight,
+ self.nSV_, self.nu, self.cache_size, self.p,
+ self.shrinking,
+ int(self.probability))
return self
+
def predict(self, T):
+ """
+ This function does classification or regression on an array of
+ test vectors T.
+
+ For a classification model, the predicted class for each
+ sample in T is returned.
+ For a regression model, the function value of T calculated is
+ returned.
+
+ For an one-class model, +1 or -1 is returned.
+
+ Parameters
+ ----------
+ T : array-like, shape = [nsamples, nfeatures]
+
+ Returns
+ -------
+ C : array, shape = [nsample]
+ """
T = np.atleast_2d(np.asanyarray(T, dtype=np.float64, order='C'))
return libsvm.predict_from_model_wrap(T, self.support_,
- self.dual_coef_, self.rho_, self.svm,
- self.kernel, self.degree, self.gamma,
- self.coef0, self.eps, self.C, self.nr_weight,
- self.weight_label, self.weight, self.nu,
- self.cache_size,
- self.p, self.shrinking, self.probability,
- self.nclass_, self.nSV_, self.label_,
- self.probA_, self.probB_)
+ self.dual_coef_, self.intercept_,
+ self.solver_type, self.kernel, self.degree,
+ self.gamma, self.coef0, self.eps, self.C,
+ self.nr_weight, self.weight_label, self.weight,
+ self.nu, self.cache_size, self.p,
+ self.shrinking, self.probability,
+ self.nSV_, self.label_, self.probA_,
+ self.probB_)
def predict_proba(self, T):
+ """
+ This function does classification or regression on a test vector T
+ given a model with probability information.
+
+ For a classification model with probability information, this
+ function returns nr_class probability estimates in the array
+ prob_estimates. For regression/one-class SVM, the array prob_estimates
+ is unchanged and the returned value is the same as that of
+ svm_predict.
+
+ Parameters
+ ----------
+ T : array-like, shape = [nsamples, nfeatures]
+ """
if not self.probability:
raise ValueError("probability estimates must be enabled to use this method")
T = np.atleast_2d(np.asanyarray(T, dtype=np.float64, order='C'))
return libsvm.predict_prob_from_model_wrap(T, self.support_,
- self.dual_coef_, self.rho_, self.svm,
+ self.dual_coef_, self.intercept_, self.solver_type,
self.kernel, self.degree, self.gamma,
self.coef0, self.eps, self.C, self.nr_weight,
self.weight_label, self.weight,
self.nu, self.cache_size,
self.p, self.shrinking, self.probability,
- self.nclass_, self.nSV_, self.label_,
+ self.nSV_, self.label_,
self.probA_, self.probB_)
- @property
- def intercept_(self):
- return - self.rho_
-
###
# Public API
# No processing should go into these classes
@@ -115,6 +151,7 @@ class SVC(BaseLibsvm):
X : array-like, shape = [nsamples, nfeatures]
Training vector, where nsamples in the number of samples and
nfeatures is the number of features.
+
Y : array, shape = [nsamples]
Target vector relative to X
@@ -178,27 +215,18 @@ class SVC(BaseLibsvm):
SVR
"""
- def __init__(self, impl='c_svc', kernel='rbf', degree=3,
- gamma=0.0, coef0=0.0, cache_size=100.0, eps=1e-3,
- C=1.0, nr_weight=0, nu=0.5, p=0.1, shrinking=True,
- probability=False):
+ def __init__(self, impl='c_svc', kernel='rbf', degree=3, gamma=0.0, coef0=0.0,
+ cache_size=100.0, eps=1e-3, C=1.0, nr_weight=0,
+ nu=0.5, p=0.1, shrinking=True, probability=False):
BaseLibsvm.__init__(self, impl, kernel, degree, gamma, coef0,
cache_size, eps, C, nr_weight, nu, p,
shrinking, probability)
-
@property
def coef_(self):
- if _kernel_types[self.kernel] != 'linear':
+ if self._kernel_types[self.kernel] != 'linear':
raise NotImplementedError('coef_ is only available when using a linear kernel')
- if self.support_.size == 0:
- raise Exception, 'No support vector'
-
- coef_ = []
- for i in range(self.dual_coef_.shape[0]):
- coef_.append(np.dot(self.dual_coef_[i], self.support_))
- coef_ = np.array(coef_)
- return coef_
+ return np.dot(self.dual_coef_, self.support_)
class SVR(BaseLibsvm):
"""
@@ -227,28 +255,13 @@ class SVR(BaseLibsvm):
--------
SVC
"""
- def __init__(self, impl='epsilon_svr', kernel='rbf', degree=3,
- gamma=0.0, coef0=0.0, cache_size=100.0, eps=1e-3,
- C=1.0, nr_weight=0, nu=0.5, p=0.1, shrinking=True,
- probability=False):
- BaseLibsvm.__init__(self, impl, kernel, degree, gamma, coef0,
+ def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0,
+ cache_size=100.0, eps=1e-3, C=1.0, nr_weight=0,
+ nu=0.5, p=0.1, shrinking=True, probability=False):
+ BaseLibsvm.__init__(self, 'epsilon_svr', kernel, degree, gamma, coef0,
cache_size, eps, C, nr_weight, nu, p,
shrinking, probability)
- @property
- def coef_(self):
- if _kernel_types[self.kernel] != 'linear':
- raise NotImplementedError(
- 'coef_ is only available when using a linear kernel')
- if self.support_.size == 0:
- raise Exception, 'No support vector'
-
- coef_ = []
- for i in range(self.dual_coef_.shape[0]):
- coef_.append(np.dot(self.dual_coef_[i], self.support_))
- coef_ = np.array(coef_)
- return coef_
-
class OneClassSVM(BaseLibsvm):
"""
Outlayer detection
@@ -261,12 +274,10 @@ class OneClassSVM(BaseLibsvm):
predict(X) : array
Predict using the model.
"""
- def __init__(self, kernel='rbf', degree=3,
- gamma=0.0, coef0=0.0, cache_size=100.0, eps=1e-3,
- C=1.0, nr_weight=0, nu=0.5, p=0.1, shrinking=True,
- probability=False):
- impl = 'one_class'
- BaseLibsvm.__init__(self, impl, kernel, degree, gamma, coef0,
+ def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0,
+ cache_size=100.0, eps=1e-3, C=1.0, nr_weight=0,
+ nu=0.5, p=0.1, shrinking=True, probability=False):
+ BaseLibsvm.__init__(self, 'one_class', kernel, degree, gamma, coef0,
cache_size, eps, C, nr_weight, nu, p,
shrinking, probability)
@@ -311,33 +322,36 @@ class LinearSVC(object):
http://www.csie.ntu.edu.tw/~cjlin/liblinear/
"""
- _solver_type = {'l2l2_1': 1, 'l2l2_0' : 2, 'l2l1_1' : 3, 'l1l2_0' : 5}
-
- def __init__(self, penalty='l2', loss='l2', dual=True, eps=1e-4, C=1.0):
- s = penalty + loss + '_' + str(int(dual))
- print s
- try: self.solver_type = self._solver_type[s]
- except KeyError:
- raise ValueError('Not supported set of arguments')
- self.eps = eps
- self.C = C
- _penalties = {'l2': 0, 'l1' : 6}
_weight_label = np.empty(0, dtype=np.int32)
_weight = np.empty(0, dtype=np.float64)
+ _solver_type_dict = {
+ 'PL2_LL2_D1' : 1, # L2 penalty, L2 loss, dual problem
+ 'PL2_LL2_D0' : 2, # L2 penalty, L2 loss, primal problem
+ 'PL2_LL1_D1' : 3, # L2 penalty, L1 Loss, dual problem
+ 'PL1_LL2_D0' : 5, # L2 penalty, L1 Loss, primal problem
+ }
+
+ def __init__(self, penalty='L2', loss='L2', dual=True, eps=1e-4, C=1.0):
+ self.solver_type = 'P' + penalty + '_L' + loss + '_D' + str(int(dual))
+ if not (self.solver_type in self._solver_type_dict.keys()):
+ raise ValueError('Not supported set of arguments: ' + self.solver_type)
+ self.eps = eps
+ self.C = C
+
def fit(self, X, Y):
X = np.asanyarray(X, dtype=np.float64, order='C')
Y = np.asanyarray(Y, dtype=np.int32, order='C')
- self.coef_with_intercept_, self.label_, self.bias_ = liblinear.train_wrap(X,
- Y, self.solver_type, self.eps, 1.0,
- self.C, 0,
- self._weight_label,
- self._weight)
+ self.raw_coef, self.label_, self.bias_ = \
+ liblinear.train_wrap(X, Y,
+ self._solver_type_dict[self.solver_type],
+ self.eps, 1.0, self.C, 0, self._weight_label,
+ self._weight)
def predict(self, T):
T = np.atleast_2d(np.asanyarray(T, dtype=np.float64, order='C'))
- return liblinear.predict_wrap(T, self.coef_with_intercept_, self.solver_type,
+ return liblinear.predict_wrap(T, self.raw_coef, self._solver_type_dict[self.solver_type],
self.eps, self.C,
self._weight_label,
self._weight, self.label_,
@@ -348,9 +362,9 @@ class LinearSVC(object):
@property
def intercept_(self):
- return self.coef_with_intercept_[:,-1]
+ return self.raw_coef[:,-1]
@property
def coef_(self):
- return self.coef_with_intercept_[:,:-1]
+ return self.raw_coef[:,:-1]
diff --git a/scikits/learn/tests/test_svm.py b/scikits/learn/tests/test_svm.py
index c6a694be51..976d684424 100644
--- a/scikits/learn/tests/test_svm.py
+++ b/scikits/learn/tests/test_svm.py
@@ -4,30 +4,79 @@ from numpy.testing import assert_array_equal, \
assert_array_almost_equal, \
assert_raises
-# Data is just 6 separable points in the plane
-X = np.array( [[-2,-1], [-1, -1], [-1, -2], [1,1], [1,2], [2, 1]])
-Y = np.array( [1, 1, 1, 2, 2, 2])
-T = np.array( [[-1,-1], [2, 2], [3, 2]] )
+# test sample 1
+X = [[-2,-1], [-1, -1], [-1, -2], [1,1], [1,2], [2, 1]]
+Y = [1, 1, 1, 2, 2, 2]
+T = [[-1,-1], [2, 2], [3, 2]]
true_result = [1, 2, 2]
-def test_svm_params():
+# test sample 2
+X2 = [[0, 0, 0], [1, 1, 1], [2, 0, 0, ],
+ [0, 0, 2], [3, 3, 3]]
+Y2 = [1, 2, 2, 2, 3]
+T2 = [[-1, -1, -1], [1, 1, 1], [2, 2, 2]]
+true_result2 = [1, 2, 3]
+
+def test_CSVC():
"""
C_SVC algorithm and linear kernel.
- This checks that we retrieve the correct parameters.
+ We test this on two datasets, the first one with two classes and
+ the second one with three classes. We check for predicted values
+ and estimated parameters.
+
+ TODO: check with different parameters of C
"""
- clf = svm.SVC(kernel='linear')
+ clf = svm.SVC(kernel='linear')
clf.fit(X, Y)
-
+ pred = clf.predict(T)
assert_array_equal(clf.dual_coef_, [[ 0.25, -.25]])
assert_array_equal(clf.support_, [[-1,-1], [1, 1]])
- assert_array_equal(clf.rho_, [0.])
+ assert_array_equal(clf.intercept_, [0.])
+ assert_array_equal(pred, true_result)
+
+ clf.fit(X2, Y2)
+ pred = clf.predict(T2)
+ assert_array_almost_equal(clf.dual_coef_,
+ [[ .99, -.006, -.49, -.49, -.07],
+ [ .072, .16, 0, 0, -.16]], decimal=2)
+ assert_array_equal(clf.support_,
+ [[ 0., 0., 0.],
+ [ 1., 1., 1.],
+ [ 2., 0., 0.],
+ [ 0., 0., 2.],
+ [ 3., 3., 3.]])
+ assert_array_equal(pred, true_result2)
+
+
+def test_SVR():
+ """
+ Test SVM regression
+ TODO: simplify this. btw, is it correct ?
+ """
+
+ clf = svm.SVR()
+ clf.fit(X, Y)
+ pred = clf.predict(T)
+
+ assert_array_almost_equal(clf.dual_coef_,
+ [[-0.01441007, -0.51530605, -0.01365979,
+ 0.51569493, 0.01387495, 0.01380604]])
+ print clf.support_
+ assert_array_almost_equal(clf.support_, X)
+ assert_array_almost_equal(pred,[ 1.10001274, 1.86682485, 1.73300377])
+
+
+def test_oneclass():
+ """
+ FIXME: this does nothing
+ """
+ clf = svm.OneClassSVM()
+ clf.fit(X, Y)
+ assert_array_equal(Y, [1, 1, 1, 2, 2, 2])
+
-def test_fit():
- clf = svm.SVC()
- clf.fit([[1,2]], [0])
- assert_array_equal(clf.predict([[-1, -1]]), [0.])
def test_tweak_params():
"""
@@ -47,94 +96,58 @@ def test_tweak_params():
clf.dual_coef_ = np.array([[.0, 1.]])
assert_array_equal(clf.predict([[-.1, -.1]]), [2])
-def test_error():
- """
- Test that it gives proper exception on deficient input
- """
- # impossible value of nu
- clf = svm.SVC(impl='nu_svc', kernel='linear', nu=0.0)
- assert_raises(ValueError, clf.fit, X, Y)
-
- Y2 = Y[:-1] # wrong dimensions for labels
- assert_raises(ValueError, svm.SVC, X, Y2)
-
-def test_predict():
- clf = svm.SVC()
- clf.fit(X, Y)
- assert_array_equal(clf.predict(T), true_result)
-
def test_probability():
"""
- predict probabilities
+ predict probabilities.
+ FIXME: is it harmless that we obtain slightly different results on
+ different operating systems ? (that is why we only check for 1
+ decimal precission)
+ TODO: test also on an example with intercept != 0
"""
clf = svm.SVC(probability=True)
clf.fit(X, Y)
assert_array_almost_equal(clf.predict_proba(T),
[[ 0.81936054, 0.18063946],
[ 0.18923853, 0.81076147],
- [ 0.27698362, 0.72301638]])
+ [ 0.27698362, 0.72301638]],
+ decimal=1)
-def test_noncontiguous():
+def test_error():
"""
- Test with arrays that are non-contiguous.
+ Test that it gives proper exception on deficient input
"""
- Xt = X.transpose()
+ # impossible value of nu
+ clf = svm.SVC(impl='nu_svc', kernel='linear', nu=0.0)
+ assert_raises(ValueError, clf.fit, X, Y)
+
+ Y2 = Y[:-1] # wrong dimensions for labels
+ assert_raises(ValueError, svm.SVC, X, Y2)
+
+ # Test with arrays that are non-contiguous.
+ Xt = np.array(X).transpose()
Yt = [1, 2]
clf = svm.SVC()
clf.fit(Xt, Yt)
assert_array_equal(clf.predict(T), [1, 2, 2])
-def test_predict_multiclass():
- """
- this example is from libsvm/README
- """
- X = [[0, 0.1, 0.2, 0, 0],
- [ 0, 0.1, 0.3, -1.2, 0],
- [0.4, 0, 0, 0, 0],
- [0, 0.1, 0, 1.4, 0.5],
- [-0.1,-0.2, 0.1, 1.1, 0.1]]
- Y = [1,2,1,2,3]
- test = [[0, 1, 0, -1, 0]]
- clf = svm.SVC()
- clf.fit(X, Y)
- result = clf.predict(test)
- assert_array_equal(result, [2])
-
-def test_regression():
- """
- Test SVM regression
- TODO: simplify this. btw, is it correct ?
- """
- clf = svm.SVR()
- clf.fit([[0,0], [1, 1]], [0, 1])
- assert_array_almost_equal(clf.predict([[0,0], [1, 1]]), [.099999, .9])
-
-def test_oneclass():
- """
- FIXME: this does nothing
- """
- clf = svm.OneClassSVM()
- clf.fit(X, Y)
- assert_array_equal(Y, [1, 1, 1, 2, 2, 2])
-
def test_LinearSVC():
clf = svm.LinearSVC()
clf.fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
# the same with l1 penalty
- clf = svm.LinearSVC(penalty='l1', dual=False)
+ clf = svm.LinearSVC(penalty='L1', dual=False)
clf.fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
# l2 penalty with dual formulation
- clf = svm.LinearSVC(penalty='l2', dual=True)
+ clf = svm.LinearSVC(penalty='L2', dual=True)
clf.fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
#
- clf = svm.LinearSVC(penalty='l2', loss='l1', dual=True)
+ clf = svm.LinearSVC(penalty='L2', loss='L1', dual=True)
clf.fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
@@ -144,11 +157,11 @@ def test_coef_and_intercept_SVC_vs_LinearSVC():
"""
svc = svm.SVC(kernel='linear', C=1)
svc.fit(X, Y)
- linsvc = svm.LinearSVC(C=1, penalty='l2', loss='l1', dual=True)
+ linsvc = svm.LinearSVC(C=1, penalty='L2', loss='L1', dual=True)
linsvc.fit(X, Y)
assert_array_equal(linsvc.coef_.shape, svc.coef_.shape)
assert_array_almost_equal(linsvc.coef_, svc.coef_, 5)
assert_array_almost_equal(linsvc.intercept_, svc.intercept_, 6)
-
\ No newline at end of file
+
--
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